Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researc... more Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.
Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet, 2020
Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesion... more Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively.
Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks, 2020
Automatic classification of color images of skin helps clinicians and dermatologists in examining... more Automatic classification of color images of skin helps clinicians and dermatologists in examining and investigating skin melanoma. In this paper, a new deep convolutional neural network-based classification method is proposed. The proposed method consists of three main steps. First, the input color images of skin are preprocessed where the region of interest (ROI) are segmented. Second, the segmented ROI images are augmented using rotation and translation transformations. Third, different deep convolutional neural network (DCNN) architectures such as Alex-net, ResNet101, and GoogleNet are utilized. The last three layers are dropped out and replaced with new layers to be more appropriate with the task of lesion classification. The performance of the proposed method has been evaluated using three different datasets, MED-NODE, DermIS & DermQuest and ISIC 2017. The proposed DCNN have fine-tuned and trained using 85%, tested and verified using 15% of the overall datasets. The proposed method significantly improved the classification process especially with modified GoogleNet where the classification accuracy was 99.29%, 99.15%, and 98.14% for MED-NODE, Der-mIS & DermQuest, and ISIC 2017 respectively. Multimedia Tools and Applications https://doi.
Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning, 2020
Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions... more Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions result in an inaccurate diagnosis due to their high similarity. Accurate classification of the skin lesions in their early stages enables dermatologists to treat the patients and save their lives. This paper proposes a model for a highly accurate classification of skin lesions. The proposed model utilized the transfer learning and pre-trained model with GoogleNet. The model parameters are used as initial values, and then these parameters will be modified through training. The latest well-known public challenge dataset, ISIC 2019, is used to test the ability of the proposed model to classify different kinds of skin lesions. The proposed model successfully classified the eight different classes of skin lesions, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, vascular lesion, and Squamous cell carcinoma. The achieved classification accuracy, sensitivity, specificity, and precision percentages are 94.92%, 79.8%, 97%, and 80.36%, respectively. The proposed model can detect images that do not belong to any one of the eight classes where these images are classified as unknown images. INDEX TERMS Melanoma classification, skin lesions, convolution neural network, GoogleNet; ISIC 2019, bootstrap multiclass SVM, transfer learning.
Skin cancer is one of most deadly diseases in humans. According to the high similarity between me... more Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic kerato-sis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS-DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.
Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, ther... more Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, there is a high similarity between different skin lesion like melanoma and nevus, which increase the difficulty of the detection and diagnosis. A reliable automated system for skin lesion classification is essential for early detection to save effort, time and human life. In this paper, an automated skin lesion classification method is proposed. In this method, a pre-trained deep learning network and transfer learning are utilized. In addition to fine-tuning and data augmentation, the transfer learning is applied to AlexNet by replacing the last layer by a softmax to classify three different lesions (melanoma, common nevus and atypical nevus). The proposed model is trained and tested using the ph2 dataset. The well-known quantative measures, accuracy, sensitivity, specificity, and precision are used in evaluating the performance of the proposed method where the obtained values of these measures are 98.61%, 98.33%, 98.93%, and 97.73%, respectively. The performance of the proposed method is compared with the existing methods where the classification rate of the proposed method outperformed the performance of the existing methods.
This paper presents a new biometric identification and authentication schema in relation to payme... more This paper presents a new biometric identification and authentication schema in relation to payment systems and ATMs. The financial sector has used ATMs as a means to make payment and offer financial services for its clients. But, security is a major issue in accessing these machines. Improving the performance of individual matchers in the aforementioned situation may not be effective. Multi-biometric systems are used to overcome this problem by providing multiple pieces of evidence of the same identity. In this paper, we have proposed the development of a fingerprint and iris fusion system which utilizes a Minutiae Matcher for fingerprint and Hamming Distance Matcher for iris with matching score level. It has been found that the proposed multimodal technique using threshold of 0.6 gave the best results. It has an accuracy of 96.67%, FAR of 0%, FRR of 5%, and run time of 32 seconds.
Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researc... more Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.
Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet, 2020
Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesion... more Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively.
Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks, 2020
Automatic classification of color images of skin helps clinicians and dermatologists in examining... more Automatic classification of color images of skin helps clinicians and dermatologists in examining and investigating skin melanoma. In this paper, a new deep convolutional neural network-based classification method is proposed. The proposed method consists of three main steps. First, the input color images of skin are preprocessed where the region of interest (ROI) are segmented. Second, the segmented ROI images are augmented using rotation and translation transformations. Third, different deep convolutional neural network (DCNN) architectures such as Alex-net, ResNet101, and GoogleNet are utilized. The last three layers are dropped out and replaced with new layers to be more appropriate with the task of lesion classification. The performance of the proposed method has been evaluated using three different datasets, MED-NODE, DermIS & DermQuest and ISIC 2017. The proposed DCNN have fine-tuned and trained using 85%, tested and verified using 15% of the overall datasets. The proposed method significantly improved the classification process especially with modified GoogleNet where the classification accuracy was 99.29%, 99.15%, and 98.14% for MED-NODE, Der-mIS & DermQuest, and ISIC 2017 respectively. Multimedia Tools and Applications https://doi.
Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning, 2020
Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions... more Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions result in an inaccurate diagnosis due to their high similarity. Accurate classification of the skin lesions in their early stages enables dermatologists to treat the patients and save their lives. This paper proposes a model for a highly accurate classification of skin lesions. The proposed model utilized the transfer learning and pre-trained model with GoogleNet. The model parameters are used as initial values, and then these parameters will be modified through training. The latest well-known public challenge dataset, ISIC 2019, is used to test the ability of the proposed model to classify different kinds of skin lesions. The proposed model successfully classified the eight different classes of skin lesions, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, vascular lesion, and Squamous cell carcinoma. The achieved classification accuracy, sensitivity, specificity, and precision percentages are 94.92%, 79.8%, 97%, and 80.36%, respectively. The proposed model can detect images that do not belong to any one of the eight classes where these images are classified as unknown images. INDEX TERMS Melanoma classification, skin lesions, convolution neural network, GoogleNet; ISIC 2019, bootstrap multiclass SVM, transfer learning.
Skin cancer is one of most deadly diseases in humans. According to the high similarity between me... more Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic kerato-sis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS-DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.
Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, ther... more Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, there is a high similarity between different skin lesion like melanoma and nevus, which increase the difficulty of the detection and diagnosis. A reliable automated system for skin lesion classification is essential for early detection to save effort, time and human life. In this paper, an automated skin lesion classification method is proposed. In this method, a pre-trained deep learning network and transfer learning are utilized. In addition to fine-tuning and data augmentation, the transfer learning is applied to AlexNet by replacing the last layer by a softmax to classify three different lesions (melanoma, common nevus and atypical nevus). The proposed model is trained and tested using the ph2 dataset. The well-known quantative measures, accuracy, sensitivity, specificity, and precision are used in evaluating the performance of the proposed method where the obtained values of these measures are 98.61%, 98.33%, 98.93%, and 97.73%, respectively. The performance of the proposed method is compared with the existing methods where the classification rate of the proposed method outperformed the performance of the existing methods.
This paper presents a new biometric identification and authentication schema in relation to payme... more This paper presents a new biometric identification and authentication schema in relation to payment systems and ATMs. The financial sector has used ATMs as a means to make payment and offer financial services for its clients. But, security is a major issue in accessing these machines. Improving the performance of individual matchers in the aforementioned situation may not be effective. Multi-biometric systems are used to overcome this problem by providing multiple pieces of evidence of the same identity. In this paper, we have proposed the development of a fingerprint and iris fusion system which utilizes a Minutiae Matcher for fingerprint and Hamming Distance Matcher for iris with matching score level. It has been found that the proposed multimodal technique using threshold of 0.6 gave the best results. It has an accuracy of 96.67%, FAR of 0%, FRR of 5%, and run time of 32 seconds.
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Papers by Mohamed A . Kassem