Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.Comment: Pattern Recognitio

A comparative study of algorithms for automatic segmentation of dermoscopic images

Melanoma is the most common as well as the most dangerous type of skin cancer. Nevertheless, it can be effectively treated if detected early. Dermoscopy is one of the major non-invasive imaging techniques for the diagnosis of skin lesions. The computer-aided diagnosis based on the processing of dermoscopic images aims to reduce the subjectivity and time-consuming analysis related to traditional diagnosis. The first step of automatic diagnosis is image segmentation. In this project, the implementation and evaluation of several methods were proposed for the automatic segmentation of lesion regions in dermoscopic images, along with the corresponding implemented phases for image preprocessing and postprocessing. The developed algorithms include methods based on different state of the art techniques. The main groups of techniques which have been selected to be studied and implemented are thresholding-based methods, region-based methods, segmentation based on deformable models, as well as a new proposed approach based on the bag-of-words model. The implemented methods incorporate modifications for a better adaptation to features associated with dermoscopic images. Each implemented method was applied to a database constituted by 724 dermoscopic images. The output of the automatic segmentation procedure for each image was compared with the corresponding manual segmentation in order to evaluate the performance. The comparison between algorithms was carried out regarding the obtained evaluation metrics. The best results were achieved by the combination of region-based segmentation based on the multi-region adaptation of the k-means algorithm and the subIngeniería de Sistemas Audiovisuale