Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images Book Chapter

Soni, J, Prabakar, N, Upadhyay, H. (2023). Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images . 134 287-303. 10.1007/978-3-031-40688-1_13

cited authors

  • Soni, J; Prabakar, N; Upadhyay, H


  • Cancer is one of the major health issues currently. Amongst different types of cancer in women, breast cancer stands in the second-highest place. Compared to other kinds of cancer, breast cancer mortality is high. The histopathological analysis is still considered the typical method of identifying cancer, even with the swift developments in medical disciplines. Analysis of histopathological images is time-consuming due to its complexity, and additionally, the results pertain to pathologist subjectivity. Therefore, developing robust and precise cancer detection using histopathological image analysis methods is essential. In this chapter, we discuss the problem of Cancer Detection using a deep learning-based approach. We provide details of the convolutional neural network (CNN), a neural network used for image datasets. Next, we describe various hyper-optimization techniques to optimize the training of the CNN network. Furthermore, we implement Cancer Detection by proposing a learning-based framework on the open-source Histopathologic Cancer Detection dataset. This dataset is available on Kaggle. The proposed framework uses CNN to detect cancer using advanced deep learning frameworks like TensorFlow. This approach can be extended to classify histopathologic images in other biomedical areas.

publication date

  • January 1, 2023

Digital Object Identifier (DOI)

start page

  • 287

end page

  • 303


  • 134