Abstract:
This research work has been done towards the treatment of Gastrointestinal (GI) Cancer by
experimenting as to how a computer aided design can help oncologists classify and segment
GI Cancer using Gastroenterology (GE) Images. The concept of Attention Module by the name
of Convolution Block Attention Module (CBAM) using Convolutional Neural Network (CNN)
layers has been utilized to achieve saliency mapping in these images. The different layers of
the CNN are tweaked and the hyper parameters are consistently checked and changed to
achieve maximum accuracy. The CBAM Algorithm is divided into Channel Attention (CA)
and Spatial Attention (SA) module. The Channel Attention has been implemented in the HSV
Color Space for finding the best color space for segmentation purposes of the Chromo
endoscopy medical images. Classification, followed by segmentation, is again implemented
using CNN based model for normal and abnormal cancer images. For classification, 92% test
accuracy has been achieved, while 70% Dice Coefficient has been obtained for segmentation.
The results achieved are competitive as compared to the previous reported results on the GE
images dataset.
