2019 International Conference on Intelligent Computing and Control Systems (ICCS)
Liver cancer is one of the terminal noncommunicable diseases. These metastatic diseases of the li... more Liver cancer is one of the terminal noncommunicable diseases. These metastatic diseases of the liver are causing each year to increase the number of deaths worldwide. World Health Organization (WHO) states that liver cancer is alone responsible for about 788000 deaths. The automatic segmentation methods have been used to improve the accuracy, a however technique with high accuracy for detection of liver deformity is highly essential. In this survey paper, a comparative analysis is carried out illuminating the benefits and drawbacks of numerous available technologies. The paper concentrates on scrutinizing the applicability of available techniques of liver segmentation of CT images. The prime purpose of this paper is to highlight the condition automated technologies that to develop novel technologies to solve case problems in the medical field.
International Journal of Innovative Technology and Exploring Engineering, 2019
The lesion size estimation is essential need while diagnosing the liver cancer and treatment scen... more The lesion size estimation is essential need while diagnosing the liver cancer and treatment scenario. The lesion segmentation suing conventional methods such as region growing, threshold based segmentation provide limited performance due to variations in light intensity distribution throughout the image. The deep learning approach used in this paper consist of input dataset of liver abdominal images along with labelled set combination of variety of liver regions and lesion structures. The care has been taken while constructing the dataset such that, the lesion due to cancer in liver of particular image should have at least one matching structure should be present in one of the labelled images. The 3 fold validation is done to evaluate the performance in which total 140 images of liver cancer are used for training, 30 images for validation and 30 images for testing. The result shows 98.5% accuracy for lesion classification. The area of lesion is compared to total area of liver in te...
In this paper smart energy meter with GSM model for prepaid and postpaid system is proposed. Due ... more In this paper smart energy meter with GSM model for prepaid and postpaid system is proposed. Due to this system the user has ability to select prepaid or postpaid mode as per his/her requirement. In prepaid mode balance is reduced as per energy is consumed. When the balance is low user gets a "Balance is low" message. If balance reaches to zero then with the help of relay power automatically gets cut off. If the account is recharged then the user gets uninterrupted power supply. In postpaid system with the help of GSM module MSEB gets the message of how much units a particular user is used and according to that data MSEB sends bill by post.
International Journal of Scientific Engineering and Technology, 2015
We describe here an efficient algorithm for re- assembling one or more unknown objects that have ... more We describe here an efficient algorithm for re- assembling one or more unknown objects that have been broken or torn into a large number N of irregular fragments. The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by in-painting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is carried out. The optimization of total affinity gives the best...
In this paper, the liver lesions classification system for CT images use deep learning (CNN)model... more In this paper, the liver lesions classification system for CT images use deep learning (CNN)model withimproved accuracy has proposed. The sequential model of CNN architecture with I/P convolution layer, Hidden convolution layer, and O/P convolution layer for CT images have been used to classify liver lesions. TensorFlow-2.0 is used to make an image with varying image qualities.The proposed network is used for CT images with an image size of 65×65, 60×60, 50×50 for which liver lesions classification accuracy of 99%,97%,95% respectivelyare achieved. The regularization technique used in proposed N/W has helped to improve the accuracy andminimization over fitting problem. The classification accuracy improvement has justified by comparing theproposed research work with other researcher's work.
2019 International Conference on Intelligent Computing and Control Systems (ICCS)
Liver cancer is one of the terminal noncommunicable diseases. These metastatic diseases of the li... more Liver cancer is one of the terminal noncommunicable diseases. These metastatic diseases of the liver are causing each year to increase the number of deaths worldwide. World Health Organization (WHO) states that liver cancer is alone responsible for about 788000 deaths. The automatic segmentation methods have been used to improve the accuracy, a however technique with high accuracy for detection of liver deformity is highly essential. In this survey paper, a comparative analysis is carried out illuminating the benefits and drawbacks of numerous available technologies. The paper concentrates on scrutinizing the applicability of available techniques of liver segmentation of CT images. The prime purpose of this paper is to highlight the condition automated technologies that to develop novel technologies to solve case problems in the medical field.
International Journal of Innovative Technology and Exploring Engineering, 2019
The lesion size estimation is essential need while diagnosing the liver cancer and treatment scen... more The lesion size estimation is essential need while diagnosing the liver cancer and treatment scenario. The lesion segmentation suing conventional methods such as region growing, threshold based segmentation provide limited performance due to variations in light intensity distribution throughout the image. The deep learning approach used in this paper consist of input dataset of liver abdominal images along with labelled set combination of variety of liver regions and lesion structures. The care has been taken while constructing the dataset such that, the lesion due to cancer in liver of particular image should have at least one matching structure should be present in one of the labelled images. The 3 fold validation is done to evaluate the performance in which total 140 images of liver cancer are used for training, 30 images for validation and 30 images for testing. The result shows 98.5% accuracy for lesion classification. The area of lesion is compared to total area of liver in te...
In this paper smart energy meter with GSM model for prepaid and postpaid system is proposed. Due ... more In this paper smart energy meter with GSM model for prepaid and postpaid system is proposed. Due to this system the user has ability to select prepaid or postpaid mode as per his/her requirement. In prepaid mode balance is reduced as per energy is consumed. When the balance is low user gets a "Balance is low" message. If balance reaches to zero then with the help of relay power automatically gets cut off. If the account is recharged then the user gets uninterrupted power supply. In postpaid system with the help of GSM module MSEB gets the message of how much units a particular user is used and according to that data MSEB sends bill by post.
International Journal of Scientific Engineering and Technology, 2015
We describe here an efficient algorithm for re- assembling one or more unknown objects that have ... more We describe here an efficient algorithm for re- assembling one or more unknown objects that have been broken or torn into a large number N of irregular fragments. The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by in-painting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is carried out. The optimization of total affinity gives the best...
In this paper, the liver lesions classification system for CT images use deep learning (CNN)model... more In this paper, the liver lesions classification system for CT images use deep learning (CNN)model withimproved accuracy has proposed. The sequential model of CNN architecture with I/P convolution layer, Hidden convolution layer, and O/P convolution layer for CT images have been used to classify liver lesions. TensorFlow-2.0 is used to make an image with varying image qualities.The proposed network is used for CT images with an image size of 65×65, 60×60, 50×50 for which liver lesions classification accuracy of 99%,97%,95% respectivelyare achieved. The regularization technique used in proposed N/W has helped to improve the accuracy andminimization over fitting problem. The classification accuracy improvement has justified by comparing theproposed research work with other researcher's work.
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