Application of Convolutional Neural Networks in Cancer Diagnosis

Abstract

Early detection and treatment are crucial for recovery from cancer. Due to rapid advancement in medical imaging techniques, today doctors can scan various body parts from different perspectives to diagnose cancer or see the progress during a course of treatment. Such techniques are less painful and require less preparation and recovery. The recent evolution of AI, particularly deep learning algorithms have shown potential in computer-aided diagnosis of cancer. These include analysis of cancer images for detection of tumor region, prognosis, skin tissue inspection, etc. Convolutional Neural Network (CNN) based image processing techniques are showing promising results in the diagnosis of breast cancer, skin cancer, prostate cancer, lung cancer, etc. Literature shows many other CNN-based models such as feed-forward neural network, Deep CNN, Multi-Crop CNN, fully convolutional networks (FCNs), Deep Fully Convolutional Network (DFCNet), Recurrent neural networks, etc. The CNN focus solely on the image-specific features and hence require a lesser number of input parameters. A CNN model reduces the size of the input image vector without losing the features critical for making an accurate prediction. This makes it easier to process and reduces computational complexity. CNNs are also not sensitive to the object positioning in an image since the approximate position of image motifs relative to others is known. CNN deals with the variations in images through augmentation and other pre-processing techniques. This makes them robust to various straining conditions in images. This chapter presents an overview of a generic CNN architecture followed by a discussion on major advances in CNN models and their applications for cancer image analysis.