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Literature Survey in Cancer Detection and Diagnosis using Deep Learning

Literature Survey in Cancer Detection and Diagnosis using Deep Learning

Top Literature Survey in Cancer Detection and Diagnosis using Deep Learning

With the tremendous success of deep learning technology in the healthcare sector, there has been great improvement in implementing deep learning-based diagnostic solutions applied to detect and diagnose cancer. Deep learning algorithms detect and diagnose cancer accurately and handle the cancer datasets more smartly.

Convolutional Neural Networks (CNN), Multi-Scale Convolutional Neural Networks (M-CNN), Fully Convolutional Networks (FCNs), U-Net Fully Convolutional Neural Networks, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LTSM), Restricted Boltzmann Machine (RBM), Autoencoders (AEs), Sparse Autoencoders (SAE), Convolutional Autoencoders (CAE), Deep Belief Networks (DBN), Multi-Scale Convolutional Neural Network (M-CNN), Multi-Instance Learning Convolutional Neural Network (MIL-CNN), Restricted Boltzmann’s Machine (RBM), Deep Autoencoders (DANs) and Adaptive Fuzzy Inference Neural Network (AFINN) are the impressive deep learning architectures utilized for a variety of cancer diagnosis.

Breast Cancer, Skin Cancer, Head and Neck Cancer, Brain Cancer, Liver Cancer, Colorectal Cancer, Ovarian Cancer, Lung Cancer, Bladder Cancer, Gastric Cancer, and Pancreatic Cancer are severe conditions of cancer detected using deep learning techniques. AI explainability and uncertainty, a Paucity of public phenotypically characterized datasets, and Data variability are challenges of deep learning models that need to focus on future improvements in cancer diagnosis.

The number of publications on deep learning for cancer detection and diagnostics is quickly increasing, and such comprehensive surveys present details of deep learning techniques, steps of cancer diagnosis, state-of-the-art deep learning models, recent advancements in cancer diagnosis, feature extraction techniques, data modalities, benchmark datasets, current challenges, and future research directions.