Inaccurate detection of tumors, fractures, and breast cancer in clinical
images has become one of the major issues in the medical field. Variations or errors in
medical reports caused by operators, machines, or the environment become a common
cause of delay or incorrect diagnosis. Therefore, correct segmentation of areas of
interest in clinical images like X-rays and MRIs is highly required. To solve this
problem, many researchers have provided various state-of-the-art automatic or semiautomatic methods of segmentation. Artificial neural networks play a significant role in
increasing the accuracy of clinical image segmentation. In this chapter, the workings of
ANN and the difference between gradient and stochastic gradient descent are
discussed. Also, the application of ANN in tumor, fracture, and breast cancer
segmentation is discussed using authentic and publically available datasets. This
chapter mentions the results and confusion matrix of some state-of-the-art methods.
This chapter will help readers know about ANN, the use of gradient and stochastic
gradient descent, the application of ANN in segmenting clinical images, and the
confusion matrix.
Keywords: ANN, Clinical images, Deep learning, Segmentation, Tumor segmentation.
