An Efficient Cancer Detection Using Machine Learning Algorithm

Abstract

Machine learning algorithms based automated systems are gaining attention for creating new device applications in the field of artificial intelligence. Especially in health care systems, implementation of emerging machine learning paradigms helps in providing a potential to raise productivity, consistency, and quality of treatment. Cancer is an epidemic that has high mortality and incidence rate worldwide. Breast cancer and oral cavity cancer are most prevailing types of cancer in females and males, respectively. Biopsy is the technique to determine the ability of cancer with confidence which includes various processing steps such as grading, staging and visual inspection. The manual analysis of histopathology slides is a labour intensive task and influenced by various factors like fatigue, attention, and expertise of pathologist. However, recent developments in soft computing techniques allow to build an automated computerized diagnostic system for cancer detection. 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. In the view of foregoing, the present work addresses the above discussed challenges and which is freely available in public domain.