Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment

© 2022 by the authors; CC BY-NC-ND license

laryngeal carcinoma; substitution voicing; voice analysis; convolutional neural networks; deep learning; breast cancer; histopathological images; computer aided diagnosis; cancer; medical imaging; deep learning; nonlinear ordinary coupled differential equation (ncode); Bernstein polynomial (bsp); genetic algorithm (ga); sliding mode controller (smc); synergetic controller (sc); chemotherapy; immunotherapy and optimization; artificial intelligence; deep learning; U-Net; PET/CT; diffuse large B-cell lymphoma; metabolic tumor volume; artificial intelligence; breast cancer; deep learning; histopathology; imaging modality; mammography; skin lesion classification; feature selection; VGG16; EfficientNet B0; ResNet50; HAM 10000 dataset; BCN 20000 dataset; skin disease; deep learning; transfer learning; attention; cost-sensitive; meta-classifier; explainable AI; class activation map; Grad-CAM; LIME; coronavirus disease; reverse transcription polymerase chain reaction; computed tomography; healthcare; health risks; adversarial propagation; liver tumor segmentation; classification; enhanced swin transformer network; median filtering; computed tomography; 4D classification; deep learning; lung nodule image; radial scanning; decision making; healthcare; breast cancer classification; histopathological images; deep learning; artificial intelligence; computer-aided diagnostics; deep learning; dermatologists; dermatology; digital dermatology; machine learning; man-machine systems; skin cancer; skin neoplasms; visual explanation; attention mechanism; human-in-the-loop deep learning; attention map; expert knowledge embedding; attention branch network; cancer diagnosis; explainable artificial intelligence; ensemble learning; Adaptive Aquila Optimizer; deep learning; bladder cancer; urothelial carcinoma; lymph node metastasis; deep learning; computed tomography (CT) imaging; machine learning; acute lymphoblastic leukemia; deep-learning; XAI; nuclei segmentation; leukemia classification; musculoskeletal X-ray; deep learning; transfer learning; data scarcity; convolution neural network (CNN); machine learning; feature fusion; gradient-based class activation heat map; artificial intelligence; machine learning; urooncology; prostate cancer; cholangioscopy; artificial intelligence; biliary strictures; n/a