Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Electrical and Electronic Engineering(IJEREEE)

Monthly Journal for Electrical and Electronic Engineering

ISSN : 2395-2717 (Online)

Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection with Simulation using CNN LSTM

Author : Mr. Imran Shaikh 1 Dr. V.K. Kadam 2

Date of Publication :31st January 2022

Abstract: Initial prediction of any kind of cancer is always advantageous for on-time medical treatment to save the patient's life. The Computer-Aided Diagnosis (CAD) tools using signal processing & image processing methods gained significant attention for immediate & accurate diagnosis using patient’s raw medical data like Magnetic Resonance Imaging (MRI), Chromatography (CT), etc. The liver cancer early detection & analysis of its grading is an important research problem. In this research, we proposed the two models semi-automatic & automatic frameworks for liver disease classification. The models perform early detection of liver cancer accurately followed by its grading analysis into different stages like stage 1 (T1), stage 2 (T2), & stage 3 (T3). The proposed framework consists of stages like pre-processing, Region of Interest (ROI) extraction, features extraction, & classification. The raw CT scans of the liver are pre-processed to remove the noises using the filtering & contrast adjustment functions. The adaptive segmentation method is designed to using binarization & morphological operations to extract the accurate ROI with the minimum computational burden. For features extraction, the text features extracted using Gray Level Co-occurrence Matrix (GLCM), shape features using geometric moment, & automatic features using Convolutional Neural Network (CNN). The hybrid form of features normalized using the min-max technique. For the classifications, we explored the classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), & Long Term Short Memory (LSTM). We investigated the semi-automated & automated systems using the publically available research dataset

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