Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Abstract

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.

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

“…The state of the art on machine learning techniques for breast cancer computer aided diagnosis offers a wide range of analysis regarding the current status of CAD systems, when image modalities used and machine learning-based classifiers are taken into consideration [38,39,42,43]. Recent review studies in [44][45][46][47] presented a thorough survey on both traditional ML and DL literature with particular application in breast cancer diagnosis. Also, the bibliographic review in study [47] provided insightful characteristics of some well-known DL networks in breast cancer diagnosis, both in primary and metastatic breast cancer.…”

An Artificial Intelligence powered Digital Inline Holographic Microscopy and Characterization Scheme

“…Further, we experimented with an increased network size having five hidden layers with 256, 128, 64, 128, and 256 number of neurons respectively. In all these networks, rectified linear unit (ReLU) was used as the activation function while mean squared error (MSE) [31][32][33][34] was used to calculate the loss. The Adam optimizer 35,36 (after iterations over other optimizers) was found to deliver better convergence and hence used to perfect the weight and biases.…”

Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

“…Given the potentially high number of radiomics features created on top of a rising number of clinical variables, powerful algorithms are needed to encompass and make all available data flourish. This is where ML algorithms thrive and have already shown tremendous results for a number of malignancies [42][43][44], but have, for now, barely been explored in ASCC. As an exception, using various ML algorithms including random forest and J48 decision trees, De Bari et al created a model predicting inguinal relapse with respective sensitivity, specificity and accuracy of 86.4%, 50% and 83.1% on the validation dataset (and superior results compared to logistic regression), highlighting the potential of such algorithms for ASCC care [45].…”