Automatic Breast Cancer Diagnostic System Using Hidden Markov Model and Modified Backpropagation

This item is published by Universitas Islam Negeri Sunan Ampel Surabaya

Lubab, Ahmad and Rini, Dian Candra and Sawiji, Asri (2018) Automatic Breast Cancer Diagnostic System Using Hidden Markov Model and Modified Backpropagation. Other thesis, UIN Sunan Ampel Surabaya.

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Breast Cancer Final Report No WM_Lubab.pdf
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Abstract

A diagnostic system needed to help doctors deal with illness. Breast cancer is a dangerous disease that can affect anyone, either women or men. Identification of can be done using a mammography tool that produces a mammogram image. In this study, The improvement or image of images in image processing is done using adaptive histogram, then followed by the segmentation process using HMM. HMM is segmented by calculating the probability values between pixels based on neighboring properties, then two dimensions HMM applied by using Viterbi training to get good features. After getting a vector feature from the HMM results, modified backpropagation utilized the use of hidden layer nodes that are randomly used. It aims to make the training process faster by optimizing linear errors and non-linear errors. The system produces the best accuracy of 80%.