Identification and Segmentation of Medical Images by Using Marker-Controlled Watershed Transformation Algorithm, XAI, and ML

Abstract

To make human life easy and compact, XAI has developed a lot with more innovations and contributed its own share. To make a suitable treatment while diagnosed with brain tumour, one needs to classify the tumour and detect it in a proper way where the explained result is most important. With the help of different analysis processes where marker-based approaches can help in proper segmentation and noise reduction analysis, numerous imaging modalities exist for tumour detection that are utilized to identify tumours in the brain. One of the most important issues of XAI system is medical diagnosis through ML in medical image processing. In this chapter, the authors present a modified marker-controlled watershed transformation approach to detect brain tumour with XAI and machine learning approaches. They include CNN and data augmentation algorithms. Image pre-processing takes the main area to detect and diagnose disease and diagnose properly. The statistical measurements have been introduced to get the mathematical abstractions of different approaches for result analysis.