Skin cancer detection: a review using deep learning techniques
Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-
repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or …
repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or …
Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges
J Yang, S Li, Z Wang, H Dong, J Wang, S Tang - Materials, 2020 - mdpi.com
The detection of product defects is essential in quality control in manufacturing. This study
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …
Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images
Skin is one of the most significant organs, which serves as a barrier to the outside
surroundings of the human body. To improve mortality, skin disease detection at a prior …
surroundings of the human body. To improve mortality, skin disease detection at a prior …
Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM
Deep learning models are efficient in learning the features that assist in understanding
complex patterns precisely. This study proposed a computerized process of classifying skin …
complex patterns precisely. This study proposed a computerized process of classifying skin …
[HTML][HTML] Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-
169 model. However, the current system for identifying metastases in a lymph node is …
169 model. However, the current system for identifying metastases in a lymph node is …
Designing deep learning studies in cancer diagnostics
A Kleppe, OJ Skrede, S De Raedt, K Liestøl… - Nature Reviews …, 2021 - nature.com
The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …
and systems are frequently claimed to perform comparable with or better than clinicians …
Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering
Melanoma skin cancer is the most life‐threatening and fatal disease among the family of
skin cancer diseases. Modern technological developments and research methodologies …
skin cancer diseases. Modern technological developments and research methodologies …
Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review
Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently,
researchers have shown an increasing interest in developing computer-aided diagnosis …
researchers have shown an increasing interest in developing computer-aided diagnosis …
AI in medical imaging informatics: current challenges and future directions
This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …
imaging informatics, discusses clinical translation, and provides future directions for …