Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment

doi:10.3390/books978-3-7258-0712-3

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Keywords

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

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Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment

Edited by

April 2024

420 pages

ISBN 978-3-7258-0711-6 (Hardback)
ISBN 978-3-7258-0712-3 (PDF)

https://doi.org/10.3390/books978-3-7258-0712-3

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This is a Reprint of the Special Issue Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment that was published in

Public Health & Healthcare

Medicine & Pharmacology

Computer Science & Mathematics

Engineering

Summary

This reprint includes a collection of recent articles on the identification, staging, diagnosis, and classification of malignant biliary structures, leukemia, bladder cancer, colorectal and osteosarcoma cancer, oral cancer, breast cancer, lung nodules, liver tumors, pulmonary diseases, skin cancer, metabolic tumor, and laryngeal carcinoma. It also includes items on the use of computer-aided, deep learning, and medical imaging-based cancer detection and diagnosis.