A survey on deep learning methods for brain tumor and liver lesion detection.

Cancer is one of the most dangerous diseases which is the cause of death of around half a million people every year. Research scientists and doctors are still finding methods to avoid these casualties. Brain tumor and Liver lesions are among the most fatal cancers and early detection of these diseases can help in reducing large human loss every year. Various methods are used for early detection of brain tumor and liver lesion from medical images using computer vision and image processing. This paper provides a detailed analysis on various methods used for detection and segmentation of brain tumor and liver lesion. Due to the large availability of image data and complex computing resources which can process them has made a deep learning approach popular for medical image analysis. Deep learning architectures like Convolutional Neural Network (CNN), Fully Convolutional Network, Auto Encoders and Generative Adversarial Network (GAN) has transformed the research in brain tumor and liver lesion analysis. This paper discusses these deep learning architectures in brief along with the various variations in these architectures for medical image analysis. The datasets used for brain tumor and liver lesion analysis are also listed and compared in the paper. The advantages, limitation and research challenges of using deep learning methods is discussed in detail along with the possible solutions for overcoming these limitations. This paper will explore possible research gaps in deep learning in terms of analyzing the medical image with different modalities, resource computation, ensemble of CNN and optimization of the deep learning model for classification and prediction of medical data. [ABSTRACT FROM AUTHOR]