EFFICIENT MACHINE LEARNING ALGORITHM FOR CANCER DETECTION USING BIOMEDICAL IMAGE

Abstract

Cancer is an epidemic that has high mortality and incidence rate worldwide. Breast cancer and oral cavity cancer is the most prevailing type of cancer in females and males, respectively. Biopsy is the only method to determine the presence of cancer with confidence which includes various processing steps such as grading, staging and visual inspection of histological slides under the microscope. The manual analysis of histopathology slides is a labor-intensive task and influenced by various factors like fatigue, attention, and expertise of pathologist. However, recent developments in soft computing techniques allow us to build an automated computer assisted diagnostic system for cancer detection which further assists pathologists in providing a reliable and consistent results on cancer diagnosis. In this context, many efforts are dedicated to feature extraction step in conventional machine learning. Deep learning is the latest advancement in this direction and opens up a new horizon in the field of machine learning. Automatic representation of the data is the key asset of the deep learning technique but require intense training and a comprehensive well-annotated dataset for their good performance. As a result of the lengthy and expensive process of collecting data from patients, obtaining large annotated datasets in health informatics is a difficult undertaking. As part of a deep learning methodology, this research proposes a CNN architecture for efficient classification. The present work proposes a new model which is applicable for both, binary as well as multi-classification of breast cancer tissue images. As a result, the suggested model does not depend on picture magnification. The beauty of this model is that the pooling layer is only present in the last convolutional layer, which aids in preventing excessive information loss. It is also possible to enhance the dataset size by using a technique called data augmentation. It's clear from a comparison of the suggested model's classification performance with the existing one that the new one is superior. To determine the influence of training in extracting significant characteristics from the images, feature maps are visually analyzed. Generalization, scalability and robustness to the image of any magnification are the powerful assets of the developed model