survey

Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

Authors:

Karen Carrasco,

Eileen Ramírez Meza,

Doris Meza Bolaños,

Washington Ramírez MontalvanAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 8

Article No.: 197, Pages 1 - 38

https://doi.org/10.1145/3648359

Published: 26 April 2024 Publication History

Abstract

The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of literature poses an obstacle to a complete understanding in this area of interest. The methodologies studied were systematic mapping and systematic literature review. For each article, the journal, conference, year of publication, dataset, breast cancer characteristics, PET/CT processing techniques, metrics and diagnostic yield results were identified. Sixty-four articles were analyzed, 44 (68.75%) belong to journals and 20 (31.25%) belong to the conference category. A total of 102 techniques were identified, which were distributed in preprocessing with 7 (6.86%), segmentation with 15 (14.71%), feature extraction with 15 (14.71%), and classification with 65 (63.73%). The techniques with the highest incidence identified in each stage are: Gaussian Filter, SLIC, Local Binary Pattern, and Support Vector Machine with 4, 2, 7, and 35 occurrences, respectively. Support Vector Machine is the predominant technique in the classification stage, due to the fact that Artificial Intelligence is emerging in medical image processing and health care to make expert systems increasingly intelligent and obtain favorable results.

References

[1]

E. Rubio, A. Noboa, E. Haro, G. Mena, and W. Mustac. 2012. Normativa de Aplicación de PET-CT. MSP-Ecuador.

[2]

PAHO. 2018. Cáncer de mama en las Américas. https://www.paho.org/sites/default/files/Cancer-mama-Americas-factsheet-ES%20%281%29.pdf

[3]

MSP-Ecuador. 2018. Cifras de Ecuador Cancer de Mama. https://www.salud.gob.ec/cifras-de-ecuador-cancer-demama/. Accessed: 9/4/2024.

[4]

S. Abirami and P. Chitra. 2020. Energy-efficient edge based real-time healthcare support system. In dvances in Computers. Elsevier, 117 (2020), 339–368. DOI:

[5]

Ibrikci Turgay Al-Salihy and Nusaibah Kh. 2017. Classifying Breast Cancer by Using Decision Tree Algorithms.144–148.DOI:

Digital Library

[6]

Al-Shami Fida’a Al-Shargabi, Bassam. 2019. An Experimental Study for Breast Cancer Prediction Algorithms. 6 pages. DOI:

Digital Library

[7]

A. Tareef, Y. Song, W. Cai, D. D. Feng, and M. Chen. 2014. Automated three-stage nucleus and cytoplasm segmentation of overlapping cells. In ICARCV. 865–870.

[8]

Ziad Alqadi. 2018. Salt and pepper noise: Effects and removal. Int. J. Electric. Eng. Inform. 2 (2018).

[9]

Robin L. Anderson, Theo Balasas, Juliana Callaghan, R. Charles Coombes, Jeff Evans, Jacqueline A. Hall, Sally Kinrade, David Jones, Paul S. Jones, and Rob Jones. 2019. A framework for the development of effective anti-metastatic agents. Nat. Rev. Clinic. Oncol. 16, 3 (2019), 185–204.

[10]

T. Araujo, G. Aresta, E. Castro, J. Rouco, P. Aguiar, C. Eloy, A. Polonia, and A. Campilho. 2017. Classification of breast cancer histology images using convolutional neural networks. PLoS One 12, 6 (2017), e0177544. DOI:

[11]

Amira S. Ashour, Yanhui Guo, and Ahmed Refaat Hawas. 2019. Neutrosophic Hough transform for blood cells nuclei detection. Neutrosophic Set in Medical Image Analysis. Academic Press, 207–227.

[12]

Jun Bai, Russell Posner, Tianyu Wang, Clifford Yang, and Sheida Nabavi. 2021. Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review. Med. Image Anal. 71 (2021), 102049. DOI:

[13]

Dalal Bardou, Kun Zhang, and Sayed Mohammad Ahmad. 2018. Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6 (2018), 24680–24693. DOI:

[14]

Flavio Baronti, Alessio Micheli, Alessandro Passaro, and Antonina Starita. 2007. Machine learning contribution to solve prognostic medical problems.Elsevier, Amsterdam, 261–283. DOI:

[15]

Rami S. Alkhawaldeh Bassam Al-Shargabi, Fida’a Al-Shami. 2020. Enhancing multi-layer perception for breast cancer prediction. Int. J. Advanc. Sci. Technol. (2020).DOI:

[16]

Ali Al Bataineh. 2019. A comparative analysis of nonlinear machine learning algorithms for breast cancer detection. Int. J. Mach. Learn. Comput. 9, 3 (2019), 248–254. DOI:

[17]

N. Bayramoglu, J. Kannala, and J. Heikkilä. 2016. Deep learning for magnification independent breast cancer histopathology image classification. In 23rd International Conference on Pattern Recognition (ICPR’16). 2440–2445. DOI:

[18]

Babak Ehteshami Bejnordi, Jimmy Lin, Ben Glass, Maeve Mullooly, Gretchen L. Gierach, Mark E. Sherman, Nico Karssemeijer, Jeroen van der Laak, and Andrew H. Beck. 2017. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. In IEEE International Symposium on Biomedical Imaging. 929–932. DOI:

[19]

Hoss Belyadi and Alireza Haghighat. 2021. Neural networks and deep learning. In ML Guide for Oil and Gas with Python. Gulf Professional Publishing, 297–347. DOI:

[20]

Arpit Bhardwaj and Aruna Tiwari. 2015. Breast cancer diagnosis using genetically optimized neural network model. Expert Syst. Applic. 42, 10 (2015), 4611–4620. DOI:

Digital Library

[21]

Verónica Bolón-Canedo and Beatriz Remeseiro. 2020. Feature selection in image analysis: A survey. Artif. Intell. Rev. 53, 4 (2020), 2905–2931. DOI:

[22]

M. Caballo, D. R. Pangallo, R. M. Mann, and I. Sechopoulos. 2020. Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence. Comput. Biol. Med. 118 (2020), 103629. DOI:

Digital Library

[23]

Carlos Cabrerizo, P. Castro, E. Corredoira, J. M. Pérez, A. Serrada, and P. Letón. 2010. Contorneo de volúmenes tumorales mediante segmentación por umbrales en imágenes PET: Influencia de la relación señal-fondo, tamaño y movimiento de la lesión. Rev. Fis. Med. 11 (2010), 91–100.

[24]

G. Cao, Y. Zhao, R. Ni, and X. Li. 2014. Contrast enhancement-based forensics in digital images. IEEE Trans. Inf. Forens. Secur. 9, 3 (2014), 515–525. DOI:

Digital Library

[25]

J. Cao, Z. Qin, J. Jing, J. Chen, and T. Wan. 2016. An automatic breast cancer grading method in histopathological images based on pixel-, object-, and semantic-level features. In IEEE 13th International Symposium on Biomedical Imaging (ISBI’16). 1151–1154. DOI:

[26]

Damien Chanal, Nadia Yousfi Steiner, Raffaele Petrone, Didier Chamagne, and Marie-Cécile Péra. 2022. Online Diagnosis of PEM Fuel Cell by Fuzzy C-Means Clustering. Elsevier, Oxford, 359–393. DOI:

[27]

J. Chang, J. Yu, T. Han, H. j. Chang, and E. Park. 2017. A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer. In IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom’17). 1–4. DOI:

[28]

C. C. Chatterjee and G. Krishna. 2019. A novel method for IDC prediction in breast cancer histopathology images using deep residual neural networks. In 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT’19). 95–100. DOI:

[29]

Leiyu Chen, Shaobo Li, Qiang Bai, Jing Yang, Sanlong Jiang, and Yanming Miao. 2021. Review of image classification algorithms based on convolutional neural networks. Remote Sensing 13, 22 (2021), 4712.

[30]

Mei Chen. 2021. Cell tracking in time-lapse microscopy image sequences. In Computer Vision for Microscopy Image Analysis. Academic Press, 101–129. DOI:

[31]

A. Cruz-Roa, H. Gilmore, A. Basavanhally, M. Feldman, S. Ganesan, N. N. C. Shih, J. Tomaszewski, F. A. Gonzalez, and A. Madabhushi. 2017. Accurate and reproducible invasive breast cancer detection in whole-slide images: A deep learning approach for quantifying tumor extent. Sci. Rep. 7 (2017), 46450. DOI:

[32]

D. W. Dodington, A. Lagree, S. Tabbarah, M. Mohebpour, A. Sadeghi-Naini, W. T. Tran, and F. I. Lu. 2021. Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Res. Treat. 186, 2 (2021), 379–389. DOI:

[33]

B. Ehteshami Bejnordi, M. Veta, and P. Johannes van Diest. 2017. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 22 (2017), 2199–2210. DOI:

[34]

Eyovel Eyassu and Michael Young. 2021. Nuclear Medicine PET/CT Head and Neck Cancer Assessment, Protocols, And Interpretation. StatPearls Publishing.

[35]

Y. Feng, L. Zhang, and Z. Yi. 2018. Breast cancer cell nuclei classification in histopathology images using deep neural networks. Int. J. Comput. Assist. Radiol. Surg. 13, 2 (2018), 179–191. DOI:

[36]

Constance Fourcade, Ludovic Ferrer, Gianmarco Santini, Noémie Moreau, Caroline Rousseau, Marie Lacombe, Camille Guillerminet, Mathilde Colombié, Mario Campone, Diana Mateus, and Mathieu Rubeaux. 2020. Combining superpixels and deep learning approaches to segment active organs in metastatic breast cancer PET images. In Engineering in Medicine and Biology Conference (EMBC’20). DOI:

[37]

F. Gallivanone, M. M. Panzeri, C. Canevari, C. Losio, L. Gianolli, F. De Cobelli, and I. Castiglioni. 2017. Biomarkers from in vivo molecular imaging of breast cancer: pretreatment (18)F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy. MAGMA 30, 4 (2017), 359–373. DOI:

[38]

Abien Fred M. Agarap. 2018. On breast cancer detection: An application of machine learning algorithms on the wisconsin diagnostic dataset. In ACM, New York, NY, USA, 5–9. DOI:

Digital Library

[39]

Z. Gandomkar, P. C. Brennan, and C. Mello-Thoms. 2018. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artif. Intell. Med. 88 (2018), 14–24. DOI:

[40]

Isabel E. Jiménez García. 2020. PET TAC en Oncología Radioterápica. NPunto 3 (2020). DOI:

[41]

B. Gecer, S. Aksoy, E. Mercan, L. G. Shapiro, D. L. Weaver, and J. G. Elmore. 2018. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recog. 84 (2018), 345–356. DOI:

Digital Library

[42]

Landis K. Griffeth. 2005. Use of PET/CT scanning in cancer patients: Technical and practical considerations. Proc. (Baylor Univ. Med. Cent.) 18, 4 (2005), 321–330. DOI:

[43]

Cristina Gámez. 2005. Tomografía por emisión de positrones (PET/TC): Presente y futuro de una nueva técnica de imagen en oncología. Cirugía Española 77, 3 (2005), 111–113. DOI:

[44]

Yeman Brhane Hagos, Vu Hoang Minh, Saed Khawaldeh, Usama Pervaiz, and Tajwar Abrar Aleef. 2018. Fast PET scan tumor segmentation using superpixels, principal component analysis and k-means clustering. Meth. Protoc. 1, 1 (2018), 7. DOI:

[45]

Hamit Altıparmak and Fatih Veysel Nurçin. 2019. Segmentation of Microscopic Breast Cancer Images for Cancer Detection (ICSCA’19). ACM, New York, NY, USA. DOI:

Digital Library

[46]

Z. Han, B. Wei, Y. Zheng, Y. Yin, K. Li, and S. Li. 2017. Breast cancer multi-classification from histopathological images with structured deep learning model. Sci. Rep. 7, 1 (2017), 4172. DOI:

[47]

Mehdi Hosseinzadeh. 2020. Robust control applications in biomedical engineering: Control of depth of hypnosis. In Ctl. Apps. for Biomed. Eng. Systems. Academic Press, 89–125.

[48]

Carlos Mello, Adriano Oliveira, and Wellington Dos Santos. 2012. Digital document analysis and processing. 1–356. https://www.researchgate.net/publication/291075814_Digital_document_analysis_and_processing

[49]

DL Stella. 2005. Spiral and multislice computed tomography of the body. Australasian Radiology 49, 4 (2005), 348–349.

[50]

A. Inaki, K. Nakajima, H. Wakabayashi, T. Mochizuki, and S. Kinuya. 2019. Fully automated analysis for bone scintigraphy with artificial neural network: Usefulness of bone scan index (BSI) in breast cancer. Ann. Nucl. Med. 33, 10 (2019), 755–765. DOI:

[51]

Niloofar Jazayeri and Hedieh Sajedi. 2019. Breast cancer diagnosis based on genomic data and extreme learning machine. SN Appl. Sci. 2, 1 (2019). DOI:

[52]

Jinn-Yi Yeh and Siwa Chan. 2018. CNN-Based CAD for Breast Cancer Classification in Digital Breast Tomosynthesis (ICGSP’18). ACM, New York, NY, USA, 26–30. DOI:

Digital Library

[53]

Weikuan Jia, Meili Sun, Jian Lian, and Sujuan Hou. 2022. Feature dimensionality reduction: A review. Complex Intelligent Systems 8, 3 (2022), 2663–2693.

[54]

Abhinav Kumar, Sanjay Kumar Singh, Sonal Saxena, K. Lakshmanan, Arun Kumar Sangaiah, Himanshu Chauhan, Sameer Shrivastava, and Raj Kumar Singh. 2020. Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Inf. Sci. 508 (2020), 405–421. DOI:

[55]

G. Kumar and P. K. Bhatia. 2014. A detailed review of feature extraction in image processing systems. In 4th International Conference on Advanced Computing & Communication Technologies. 5–12. DOI:

Digital Library

[56]

Salem Saleh Al-amri, N. V. Kalyankar, and S. D. Khamitkar. 2010. A Comparative Study of Removal Noise from Remote Sensing Image. arXiv:1002.1148.

[57]

Michele Larobina and Loredana Murino. 2014. Medical image file formats. J. Digit. Imag. 27, 2 (2014), 200–206. DOI:

[58]

J. Lee, R. M. Nishikawa, I. Reiser, and J. M. Boone. 2017. Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT. Med. Phys. 44, 5 (2017), 1846–1856. DOI:

[59]

Tzu C. Lee, Adam M. Alessio, Robert M. Miyaoka, and Paul E. Kinahan. 2016. Morphology supporting function: Attenuation correction for SPECT/CT, PET/CT, and PET/MR imaging. Quart. J. Nucl. Med. Molec. Imag. 60, 1 (2016), 25–39. Retrieved from https://pubmed.ncbi.nlm.nih.gov/26576737

[60]

F. Li, Y. Yang, Y. Wei, P. He, J. Chen, Z. Zheng, and H. Bu. 2021. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl. Med. 19, 1 (2021), 348. DOI:

[61]

H. Liu, Y. Liu, Q. Li, H. Liu, and Y. Tong. 2011. Medical image segmentation based on contourlet transform and watershed algorithm. In IEEE International Symposium on IT in Medicine and Education. 224–227. DOI:

[62]

Samanthe M. Lyons, Elaheh Alizadeh, Joshua Mannheimer, Katherine Schuamberg, Jordan Castle, Bryce Schroder, Philip Turk, Douglas Thamm, and Ashok Prasad. 2016. Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas. Biol. Open 5, 3 (2016), 289–299. DOI:

[63]

Miguel Martín, Ana Herrero, and Isabel J. Arbor Echavarría. 2015. El cáncer de mama. 191, 773 (2015), a234–a234. https://arbor.revistas.csic.es/index.php/arbor/article/download/2037/2531

[64]

Juan Daniel Martínez Díaz, Verónica Ortega Chacón, and Francisco José Muñoz Ronda. 2016. El diseño de preguntas clínicas en la práctica basada en la evidencia: Modelos de formulación. J Enfermería Global. 15 (2016), 431–438. Retrieved from http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1695-61412016000300016&nrm=iso

[65]

P. McLeod and B. Verma. 2015. Polynomial prediction of neurons in neural network classifier for breast cancer diagnosis. In 11th International Conference on Natural Computation (ICNC’15). 775–780. DOI:

[66]

M. M. Mehdy, P. Y. Ng, E. F. Shair, N. I. M. Saleh, and C. Gomes. 2017. Artificial neural networks in image processing for early detection of breast cancer. Comput. Math. Methods. Med. 2017 (2017), 2610628. DOI:

[67]

Francisco Melo. 2013. Area under the ROC Curve. Springer New York, NY, 38–39. DOI:

[68]

E. Mercan, S. Mehta, J. Bartlett, L. G. Shapiro, D. L. Weaver, and J. G. Elmore. 2019. Assessment of machine learning of breast pathology structures for automated differentiation of breast cancer and high-risk proliferative lesions. JAMA Netw. Open 2, 8 (2019), e198777. DOI:

[69]

S. Misra and H. Li. 2020. Noninvasive fracture characterization based on the classification of sonic wave travel times. In ML for Subsurface Char. Gulf Prof. Publ., 243–287.

[70]

N. Moreau, C. Rousseau, C. Fourcade, G. Santini, L. Ferrer, M. Lacombe, C. Guillerminet, M. Campone, M. Colombié, M. Rubeaux, and N. Normand a. 2020. Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer. In 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC’20). 1532–1535. DOI:

[71]

Ghulam Murtaza, Liyana Shuib, Ainuddin Wahid Abdul Wahab, Ghulam Mujtaba, Ghulam Mujtaba, Henry Friday Nweke, Mohammed Ali Al-garadi, Fariha Zulfiqar, Ghulam Raza, and Nor Aniza Azmi. 2019. Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges. Artif. Intell. Rev. 53, 3 (2019), 1655–1720. DOI:

[72]

Ghulam Murtaza, Liyana Shuib, Ghulam Mujtaba, and Ghulam Raza. 2019. Breast cancer multi-classification through deep neural network and hierarchical classification approach. Multim. Tools Applic. 79, 21-22 (2019), 15481–15511. DOI:

Digital Library

[73]

A. A. Nahid, F. B. Ali, and Y. Kong. 2017. Histopathological breast-image classification with image enhancement by convolutional neural network. In 20th International Conference of Computer and Information Technology (ICCIT’17). 1–6. DOI:

[74]

A. A. Nahid and Y. Kong. 2017. Local and global feature utilization for breast image classification by convolutional neural network. In International Conference on Digital Image Computing: Techniques and Applications (DICTA’17). 1–6. DOI:

[75]

A. A. Nahid and Y. Kong. 2017. Involvement of machine learning for breast cancer image classification: A survey. Comput. Math. Methods Med. 2017 (2017), 3781951. DOI:

[76]

Abdullah-Al Nahid and Yinan Kong. 2018. Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information 9, 1 (2018). DOI:

[77]

A. A. Nahid, M. A. Mehrabi, and Y. Kong. 2018. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed. Res. Int. 2018 (2018), 2362108. DOI:

[78]

M. Nakajo, M. Jinguji, A. Tani, H. Kikuno, D. Hirahara, S. Togami, H. Kobayashi, and T. Yoshiura. 2021. Application of a machine learning approach for the analysis of clinical and radiomic features of pretreatment [(18)F]-FDG PET/CT to predict prognosis of patients with endometrial cancer. Molec. Imag. Biol. 23, 5 (2021), 756–765. DOI:

[79]

Elaheh Mahraban Nejad, Lilly Suriani Affendey, Rohaya Binti Latip, and Iskandar Bin Ishak. 2017. Classification of Histopathology Images of Breast into Benign and Malignant using a Single-Layer Convolutional Neural Network (ICISPC’17). ACM, New York, NY, USA.

Digital Library

[80]

Mehrbakhsh Nilashi, Othman Ibrahim, Hossein Ahmadi, and Leila Shahmoradi. 2017. A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat. Inform. 34, 4 (2017), 133–144. DOI:

Digital Library

[81]

O. I. Obaid, M. A. Mohammed, M. K. A. Ghani, A. Mostafa, and F. Taha. 2018. Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. Int. J. Eng. Technol. 7, 4–36 (2018), 160–166. https://www.downloadmaghaleh.com/wp-content/uploads/edd/maghaleh/1398/yosefi.wisconsin-min.pdf

[82]

Chulwoo Pack, Sung Shin, Hyung-Do Choi, Soon-Ik Jeon, and John Kim. 2016. Optimized Multilayer Perceptron using Dynamic Learning Rate based Microwave Tomography Breast Cancer Screening (SAC’16). ACM, New York, NY, USA.

Digital Library

[83]

N. L. Serna Palomino and U. N. R. Concha. 2009. Técnicas de segmentación en procesamiento digital de imágenes. Rev. Investig. Sist. Inf. 6, 2 (2009), 9–16. https://revistasinvestigacion.unmsm.edu.pe/index.php/sistem/article/download/3299/2749/

[84]

Mira Park, Jesse S. Jin, Min Xu, W. S. Felix Wong, Suhuai Luo, and Yue Cui. 2009. Microscopic Image Segmentation based on Color Pixels Classification (ICIMCS’09). ACM, New York, NY, USA.

Digital Library

[85]

Cecilia Maria Patino and Juliana Carvalho Ferreira. 2018. Inclusion and exclusion criteria in research studies: Definitions and why they matter. Jornal Brasileiro de Pneumologia: Publicacao Oficial da Sociedade Brasileira de Pneumologia e Tisilogia 44, 2 (2018), 84–84. DOI:

[86]

Héctor Pereira Recio and Lizandra Pereira Corzo. 2006. Imagenología molecular: Un nuevo paradigma en Radiología. Hum. Méd., 6 (2006). http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1727-81202006000300008&lng=es

[87]

Kai Petersen, Robert Feldt, Shahid Mujtaba, and Michael Mattsson. 2008. Systematic mapping studies in software engineering. In Proc. 12th Int. Conf. on Eval. and Assess. in Software Eng.

[88]

Mahdi Bagheri, Mehdi Madani, Ramin Sahba, and Amin Sahba. 2011. Real time object detection using a novel adaptive color thresholding method. ACM, New York, NY, USA.

Digital Library

[89]

T. A. Qureshi, H. Veeraraghavan, J. S. Sung, J. B. Kaplan, J. Flynn, E. S. Tonorezos, S. L. Wolden, E. A. Morris, K. C. Oeffinger, M. C. Pike, and C. S. Moskowitz. 2019. Automated breast density measurements from chest computed tomography scans. J. Med. Syst. 43, 8 (2019), 242. DOI:

Digital Library

[90]

Nanmaran R and Luminasree B.2022. Development of wavelet transform-based image fusion technique with improved PSNR for CT and PET images in comparison with discrete cosine transform-based image fusion technique. ECS Trans. 107, 1 (2022), 13185–13193. DOI:

[91]

Keerthana Rajendran, Manoj Jayabalan, Vinesh Thiruchelvam, and V. Sivakumar. 2019. Feasibility study on data mining techniques in diagnosis of breast cancer. Int. J. Mach. Learn. Comput. 9, 3 (2019), 328–333. DOI:

[92]

M. S. Reza and J. Ma. 2018. Imbalanced histopathological breast cancer image classification with convolutional neural network. In 14th IEEE International Conference on Signal Processing (ICSP’18). 619–624. DOI:

[93]

Richard H. Wiggins II, H. Christian Davidson, H. Ric Harnsberger, Jason R. Lauman, and Patricia A. Goede. 2001. Image file formats: Past, present, and future. RadioGraphics 21, 3 (2001), 789–798. DOI:

[94]

Emilio Rivera Ledesma, Aliusca Fornaris Hernández, Eida Rosa Mariño Membribes, Keny Alfonso Díaz, Regla María Ledesma Santiago, and Isabel Cristina Abreu Carter. 2019. Factores de riesgo del cáncer de mama en un consultorio de la atención primaria de salud. Revista habanera de ciencias Médicas, 18 (2019), 308–322. Retrieved from http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1729-519X2019000200308&nrm=iso

[95]

Rahimeh Rouhi, Mehdi Jafari, Shohreh Kasaei, and Peiman Keshavarzian. 2015. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst. Applic. 42, 3 (2015), 990–1002. DOI:

Digital Library

[96]

Hanaa Salem, Gamal Attiya, and Nawal El-Fishawy. 2016. Early diagnosis of breast cancer by gene expression profiles. Pattern Anal. Applic. 20, 2 (2016), 567–578. DOI:

Digital Library

[97]

Shallu and Rajesh Mehra. 2018. Breast cancer histology images classification: Training from scratch or transfer learning. ICT Expr. 4, 4 (2018), 247–254. DOI:

[98]

G. Shamai, Y. Binenbaum, R. Slossberg, I. Duek, Z. Gil, and R. Kimmel. 2019. Artificial intelligence algorithms to assess hormonal status from tissue microarrays in patients with breast cancer. JAMA Netw. Open 2, 7 (2019), e197700. DOI:

[99]

R. Singh, T. Ahmed, A. Kumar, A. K. Singh, A. K. Pandey, and S. K. Singh. 2021. Imbalanced breast cancer classification using transfer learning. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 1 (2021), 83–93. DOI:

Digital Library

[100]

B. I. Song. 2021. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 28, 3 (2021), 664–671. DOI:

[101]

F. A. Spanhol, L. S. Oliveira, P. R. Cavalin, C. Petitjean, and L. Heutte. 2017. Deep features for breast cancer histopathological image classification. In IEEE International Conference on Systems, Man, and Cybernetics (SMC’17). 1868–1873. DOI:

Digital Library

[102]

Fabio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte. 2016. Breast cancer histopathological image classification using convolutional neural networks. In International Joint Conference on Neural Networks (IJCNN’16). 2560–2567. DOI:

[103]

F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte. 2016. A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 7 (2016), 1455–1462. DOI:

[104]

Abdulhamit Subasi. 2020. Data preprocessing. In Practical ML for Data Analysis with Python, Academic Press, 27–89. DOI:

[105]

W. Sun, T. L. Tseng, J. Zhang, and W. Qian. 2016. Computerized breast cancer analysis system using three stage semi-supervised learning method. Comput. Methods. Programs Biomed. 135 (2016), 77–88. DOI:

Digital Library

[106]

H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray. 2021. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 3 (2021), 209–249. DOI:

[107]

M. Tabb and N. Ahuja. 1997. Multiscale image segmentation by integrated edge and region detection. IEEE Trans. Image Process. 6, 5 (1997), 642–655. DOI:

Digital Library

[108]

N. Tatbul, T. J. Lee, S. Zdonik, M. Alam, and J. Gottschlich. 2018. Precision and recall for time series. In Advances in Neural Inf. Process. Syst. Curran Associates, Inc. [CrossRef], 31 (2018).

[109]

M. Urano, Y. Maki, H. Nishikawa, T. Kawai, N. Shiraki, and Y. Shibamoto. 2017. Diagnostic utility of a computer-aided diagnosis system for whole-body bone scintigraphy to detect bone metastasis in breast cancer patients. Ann. Nucl. Med. 31, 1 (2017), 40–45. DOI:

[110]

B. Vaarwerk, W. B. Breunis, L. M. Haveman, B. de Keizer, N. Jehanno, L. Borgwardt, R. R. van Rijn, H. van den Berg, J. F. Cohen, E. C. van Dalen, and et al.2021. Fluorine‐18‐fluorodeoxyglucose (FDG) positron emission tomography (PET) computed tomography (CT) for the detection of bone, lung, and lymph node metastases in rhabdomyosarcoma. Cochr. Datab. System. Rev.11 (2021). DOI:

[111]

Vaibhaw, J. Sarraf, and P. K. Pattnaik. 2020. Brain-computer interfaces and their applications. Industrial IoT Approach for Pharma Industry Growth. Academic Press, 31–54. DOI:

[112]

Scott Valastyan and Robert A. Weinberg. 2011. Tumor metastasis: Molecular insights and evolving paradigms. Cell 147, 2 (2011), 275–292.

[113]

Tao Wan, Jiajia Cao, Jianhui Chen, and Zengchang Qin. 2017. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229 (2017), 34–44. DOI:

Digital Library

[114]

Justin L. Wang, Ali K. Ibrahim, Hanqi Zhuang, Ali Muhamed Ali, Anthony Y. Li, and Andrew Wu. 2018. A study on automatic detection of IDC breast cancer with convolutional neural networks. In Proc. 2018 Int. Conf. Comput. Sci. Comput. Intell. (CSCI’18). 703–708. DOI:

[115]

H. Wang, Y. Li, and Z. Luo. 2020. An improved breast cancer nuclei segmentation method based on UNet++. In Proc. 2020 6th Int. Conf. Comput. Artif. Intell. (ICCAI’20). 193–197. DOI:

Digital Library

[116]

Pin Wang, Xianling Hu, Yongming Li, Qianqian Liu, and Xinjian Zhu. 2016. Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Sig. Process. 122 (2016), 1–13. DOI:

Digital Library

[117]

Bianca Williams, Caroline Halloin, Wiebke Löbel, Ferdous Finklea, Elizabeth Lipke, Robert Zweigerdt, and Selen Cremaschi. 2020. Data-driven Model Development for Cardiomyocyte Production Experimental Failure Prediction. Vol. 48. Elsevier, 1639–1644. DOI:

[118]

N. I. R. Yassin, S. Omran, E. M. F. El Houby, and H. Allam. 2018. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Comput. Meth. Programs Biomed. 156 (2018), 25–45. DOI:

[119]

Wenbin Yue, Zidong Wang, Hongwei Chen, Annette Payne, and Xiaohui Liu. 2018. Machine learning with applications in breast cancer diagnosis and prognosis. Designs 2, 2 (2018). DOI:

[120]

Jiadong Zhang, Jiaojiao Wu, Xiang Sean Zhou, Feng Shi, and Dinggang Shen. 2023. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin. Cancer Biol. 96 (2023), 11–25. DOI:

[121]

Xiulian Zheng, Hong Ye, and Yinggan Tang. 2017. Image bi-level thresholding based on gray level-local variance histogram. 19, 5 (2017). https://www.mdpi.com/1099-4300/19/5/191

[122]

Yushan Zheng, Zhiguo Jiang, Fengying Xie, Haopeng Zhang, Yibing Ma, Huaqiang Shi, and Yu Zhao. 2017. Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification. Pattern Recog. 71 (2017), 14–25. DOI:

[123]

Wen Zhu, Nancy Zeng, Ning Wang, et al. 2010. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proc.: Health Care and Life Sciences, Baltimore, MD 19 (2010), 67. https://www.lexjansen.com/nesug/nesug10/hl/hl07.pdf

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Izzat MSa'dan SMohd Bahrin UHatie Ishak SSyed Yasin SIbrahim NNor M(2024)Pneumonia Detection System Using Convolutional Neural Network2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS63860.2024.10730671(1-6)Online publication date: 3-Sep-2024
https://doi.org/10.1109/AiDAS63860.2024.10730671

Index Terms

Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 8

August 2024

963 pages

EISSN:1557-7341

DOI:10.1145/3613627

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
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Michela Milano
University of Bologna, Italy

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Publication History

Published: 26 April 2024

Online AM: 28 February 2024

Accepted: 31 January 2024

Revised: 11 January 2024

Received: 11 March 2023

Published in CSUR Volume 56, Issue 8

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Cited By

Izzat MSa'dan SMohd Bahrin UHatie Ishak SSyed Yasin SIbrahim NNor M(2024)Pneumonia Detection System Using Convolutional Neural Network2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS63860.2024.10730671(1-6)Online publication date: 3-Sep-2024
https://doi.org/10.1109/AiDAS63860.2024.10730671

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