Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review
D Painuli, S Bhardwaj - Computers in Biology and Medicine, 2022 - Elsevier
Being a second most cause of mortality worldwide, cancer has been identified as a perilous
disease for human beings, where advance stage diagnosis may not help much in …
disease for human beings, where advance stage diagnosis may not help much in …
Image augmentation techniques for mammogram analysis
Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …
progressively contingent. Scientific findings reveal that supervised deep learning methods' …
Sentiment analysis of customer feedback and reviews for airline services using language representation model
The competitive airline sector has grown at a breakneck pace in the last two decades. A
useful source for collecting consumer feedback and performing various forms of analysis on …
useful source for collecting consumer feedback and performing various forms of analysis on …
A hybrid workflow of residual convolutional transformer encoder for breast cancer classification using digital X-ray mammograms
Breast cancer, which attacks the glandular epithelium of the breast, is the second most
common kind of cancer in women after lung cancer, and it affects a significant number of …
common kind of cancer in women after lung cancer, and it affects a significant number of …
Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance
AS Elkorany, ZF Elsharkawy - Scientific Reports, 2023 - nature.com
Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be
saved by its early discovery. Mammography is frequently used to diagnose BC. The …
saved by its early discovery. Mammography is frequently used to diagnose BC. The …
ETECADx: Ensemble self-attention transformer encoder for breast cancer diagnosis using full-field digital X-ray breast images
Early detection of breast cancer is an essential procedure to reduce the mortality rate among
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
Deep ensemble transfer learning-based framework for mammographic image classification
This research intends to provide a method for clinical decision support systems that can
accurately classify benign and malignant mass from breast X-ray images. The model was …
accurately classify benign and malignant mass from breast X-ray images. The model was …
Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
Due to its high prevalence and wide dissemination, breast cancer is a particularly
dangerous disease. Breast cancer survival chances can be improved by early detection and …
dangerous disease. Breast cancer survival chances can be improved by early detection and …
Enhancing small medical dataset classification performance using GAN
Developing an effective classification model in the medical field is challenging due to limited
datasets. To address this issue, this study proposes using a generative adversarial network …
datasets. To address this issue, this study proposes using a generative adversarial network …
MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue
S Çayır, G Solmaz, H Kusetogullari, F Tokat… - Neural Computing and …, 2022 - Springer
Mitosis assessment of breast cancer has a strong prognostic importance and is visually
evaluated by pathologists. The inter, and intra-observer variability of this assessment is high …
evaluated by pathologists. The inter, and intra-observer variability of this assessment is high …