Prediction of abnormal hepatic region using ROI thresholding based segmentation and deep learning based classification
by Shubham Kamlesh Shah; Ruby Mishra; Bhabani Shankar Prasad Mishra; Om Pandey
International Journal of Computer Applications in Technology (IJCAT), Vol. 64, No. 4, 2020

Abstract: This paper proposes a novel Computer-Aided Diagnosis System (CADS) model using Artificial Intelligence (AI) to segment liver form abdomen CT scan. Deep Learning Convolutional Neural Network (DL-CNN) model is proposed to train the program to classify normal and abnormal liver images. For training data set generation, another novel Region of Interest (ROI) based thresholding image processing technique is proposed. DL-CNN network is also compared with the basic CNN model to understand the difference between basic and deep learning networks. The basic CNN model yielded an accuracy of 50.00% while the DL-CNN model achieved an accuracy of 98.75%. It is also compared with other existing models like AlexNet, AdaBoostM1 and classifiers such as naïve Bayes, KNN, SVM and random forest classifier models. This model will be useful for provincial hospitals in diagnosing patients, it will also help radiologists in providing more accurate and faster diagnosis by reducing human errors.

Online publication date: Thu, 28-Jan-2021

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