Abstract
Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.
Highlights
Over the last decades, three different approaches have been practiced to deal with medical images.The first is creating awareness among the community for a regular check-up and it was not be practiced among communities
Most of the performance metrics encountered in the review include area under curve (AUC), sensitivity (Sn), specificity (Sp), accuracy (Acc), precision (P), recall (R), positive predictive values (PPV), Matthews correlation coefficient (MCC), geometric mean (G-Mean), which are usually successful in describing the classification performance [8,9]
A Bayesian optimization technique was used to tune the hyperparameters of the model. They have verified that all the calculated performance metrics—i.e., accuracy, precision, sensitivity, specificity, F1-score, MCC, G-Mean of the experimental results are higher than 98% for classifying the types of brain tumors on the testing dataset obtained from Nanfang Hospital and Tianjin Medical University General Hospital which is an open-source dataset downloaded from [107]
Summary
Three different approaches have been practiced to deal with medical images. As claimed by Litjens et al [4], convolutional neural network-based deep learning has become a method for medical image analysis In their survey paper, they considered papers that were related to medical image analysis, for image classification, object detection, segmentation, registration and other tasks. Suzuki [6] in his survey paper overviewed the area of deep learning and its application in medical imaging analysis to assess what was changed before and after the introduction of deep learning in machine learning, identifying the reasons that make deep learning powerful and their applications to medical image analysis In this survey paper, we briefly describe the breast cancer, cervical cancer, brain tumor, colon cancer and lung cancer along with their respective screening methods.
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