Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …
units are used to extract patterns from data in order to solve complex problems. Deep …
A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
Classification of breast cancer using transfer learning and advanced al-biruni earth radius optimization
Breast cancer is one of the most common cancers in women, with an estimated 287,850 new
cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The …
cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The …
Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis
J Zheng, D Lin, Z Gao, S Wang, M He, J Fan - IEEE Access, 2020 - ieeexplore.ieee.org
Breast cancer is one of the most dangerous diseases and the second largest cause of
female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow …
female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow …
Convolutional neural networks for breast cancer detection in mammography: A survey
Despite its proven record as a breast cancer screening tool, mammography remains labor-
intensive and has recognized limitations, including low sensitivity in women with dense …
intensive and has recognized limitations, including low sensitivity in women with dense …
Mammogram breast cancer CAD systems for mass detection and classification: a review
Although there is an improvement in breast cancer detection and classification (CAD) tools,
there are still some challenges and limitations that need more investigation. The significant …
there are still some challenges and limitations that need more investigation. The significant …
Deep convolutional neural networks for mammography: advances, challenges and applications
D Abdelhafiz, C Yang, R Ammar, S Nabavi - BMC bioinformatics, 2019 - Springer
Background The limitations of traditional computer-aided detection (CAD) systems for
mammography, the extreme importance of early detection of breast cancer and the high …
mammography, the extreme importance of early detection of breast cancer and the high …
Breast cancer detection in mammography images: a CNN-based approach with feature selection
The prompt and accurate diagnosis of breast lesions, including the distinction between
cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast …
cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast …
A survey on adversarial deep learning robustness in medical image analysis
KD Apostolidis, GA Papakostas - Electronics, 2021 - mdpi.com
In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …
Medical image based breast cancer diagnosis: State of the art and future directions
The intervention of medical imaging has significantly improved early diagnosis of breast
cancer. Different radiological and microscopic imaging modalities are frequently utilized by …
cancer. Different radiological and microscopic imaging modalities are frequently utilized by …