AUTOMATIC COMPUTER AIDED DIAGNOSIS FRAMEWORK OF LIVER CANCER DETECTION USING CNN-LSTM
Mr. Imran Shaikh
Department of Electronics and Telecommunication Engineering, Govt, Polytechnic, Osmanabad
Dr. V.K. Kadam
Associate Professor and HOD (Electronics and Telecommunication), P.E.S. College of Engineering, Aurangabad, Shaikh.imran7@gmail.com, vipulsangram@pescoe.ac.in
Abstract
Liver cancer detection using the computer vision methods and machine learning already received significant attention of researchers for accurate diagnosis and on-time medical attentions. The Computer Aided Diagnosis (CAD) preferred for cancer detection all over the world which is based on image processing functions. Earlier CAD tools were designed using conventional machine learning methods using semi-automatic approach. The recent growth of deep learning for automatic detection and classification leads to significant improvement in accuracy. This paper proposed the automatic CAD framework for liver cancer detection using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The input Computed Tomography (CT) scan images first pre-processed for quality enhancement. After that we applied the lightweight and accuracy Region of Interest (ROI) extraction technique using dynamic binary segmentation. From ROI images, we extracted automated CNN-based features and hand-craft features. The combination of both features formed unique feature set for classification purpose. The LSTM block is then perform the classification either into normal or diseased CT image. The CNN-LSTM model is designed in this paper to enhance the accuracy of liver cancer detection compared to other deep learning solutions. The experimental results of proposed model using CNN-based features and hybrid hand craft features outperformed the recent state-of-art methods.
Keywords:
Computer tomography, computer aided diagnosis, convolutional neural network, deep learning, features extraction, segmentation, and liver cancer.
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