Diagnostic strategies for breast cancer detection: from image generation to classification strategies using artificial intelligence algorithms
JA Basurto-Hurtado, IA Cruz-Albarran… - Cancers, 2022 - mdpi.com
Simple Summary With the recent advances in the field of artificial intelligence, it has been
possible to develop robust and accurate methodologies that can deliver noticeable results in …
possible to develop robust and accurate methodologies that can deliver noticeable results in …
The role of different retinal imaging modalities in predicting progression of diabetic retinopathy: A survey
Diabetic retinopathy (DR) is a devastating condition caused by progressive changes in the
retinal microvasculature. It is a leading cause of retinal blindness in people with diabetes …
retinal microvasculature. It is a leading cause of retinal blindness in people with diabetes …
Automated deep learning approach for classification of malignant melanoma and benign skin lesions
W Salma, AS Eltrass - Multimedia Tools and Applications, 2022 - Springer
Skin cancer becomes a significant health problem worldwide with an increasing incidence
over the past decades. Due to the fine-grained differences in the appearance of skin lesions …
over the past decades. Due to the fine-grained differences in the appearance of skin lesions …
Deep learning empowered breast cancer diagnosis: Advancements in detection and classification
Recent advancements in AI, driven by big data technologies, have reshaped various
industries, with a strong focus on data-driven approaches. This has resulted in remarkable …
industries, with a strong focus on data-driven approaches. This has resulted in remarkable …
Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures
AS Eltrass, MB Tayel, AI Ammar - Neural Computing and Applications, 2022 - Springer
Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
[PDF][PDF] Breast Lesions Detection and Classification via YOLO-Based Fusion Models.
A Baccouche, B Garcia-Zapirain… - Computers …, 2021 - pdfs.semanticscholar.org
With recent breakthroughs in artificial intelligence, the use of deep learning models
achieved remarkable advances in computer vision, ecommerce, cybersecurity, and …
achieved remarkable advances in computer vision, ecommerce, cybersecurity, and …
[HTML][HTML] A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection
A robust medical decision support system for classifying skin lesions from dermoscopy
images is a crucial instrument for determining skin cancer prognosis. In recent years, full …
images is a crucial instrument for determining skin cancer prognosis. In recent years, full …
An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks
A computer-aided diagnosis (CAD) system requires automated stages of tumor detection,
segmentation, and classification that are integrated sequentially into one framework to assist …
segmentation, and classification that are integrated sequentially into one framework to assist …
Deep learning capabilities for the categorization of microcalcification
K Kumar Singh, S Kumar, M Antonakakis… - International Journal of …, 2022 - mdpi.com
Breast cancer is the most common cancer in women worldwide. It is the most frequently
diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of …
diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of …
An efficient deep neural network based abnormality detection and multi-class breast tumor classification
Breast tumor is one of the major cause of death among women all over the world.
Ultrasound imaging-based breast abnormality detection and classification play a vital role to …
Ultrasound imaging-based breast abnormality detection and classification play a vital role to …