Systematic review of computing approaches for breast cancer detection based computer aided diagnosis using mammogram images

DA Zebari, DA Ibrahim, DQ Zeebaree… - Applied Artificial …, 2021 - Taylor & Francis
Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …

The role of different retinal imaging modalities in predicting progression of diabetic retinopathy: A survey

M Elsharkawy, M Elrazzaz, A Sharafeldeen, M Alhalabi… - Sensors, 2022 - mdpi.com
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 …

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 …

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 …

[HTML][HTML] A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection

D Adla, GVR Reddy, P Nayak, G Karuna - Healthcare Analytics, 2023 - Elsevier
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 …

A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography

M Moghbel, CY Ooi, N Ismail, YW Hau… - Artificial Intelligence …, 2020 - Springer
Mammography can be considered as the current gold standard for detecting early signs of
breast cancer and is in wide use throughout the world. As confirmed by many studies, breast …

Multi-class breast cancer histopathological image classification using multi-scale pooled image feature representation (MPIFR) and one-versus-one support vector …

D Clement, E Agu, MA Suleiman, J Obayemi… - Applied Sciences, 2022 - mdpi.com
Breast cancer (BC) is currently the most common form of cancer diagnosed worldwide with
an incidence estimated at 2.26 million in 2020. Additionally, BC is the leading cause of …

Breast cancer classification and proof of key artificial neural network terminologies

N Ali, S Ansari, Z Halim, RH Ali… - … Science and Statistics …, 2019 - ieeexplore.ieee.org
Classification is one of the interesting areas in the academic field of Neural Networks.
Artificial Neural Networks (ANNs) have been extensively used in pattern recognition and …

A review on optimization techniques for medical image analysis

P Kaur, RK Singh - Concurrency and Computation: Practice …, 2023 - Wiley Online Library
Data mining of medical imaging approaches makes it difficult to determine their value in the
disease's insight, analysis, and diagnosis. Image classification presents a significant …

Fully automated scheme for computer‐aided detection and breast cancer diagnosis using digitised mammograms

AS Eltrass, MS Salama - IET Image Processing, 2020 - Wiley Online Library
Breast cancer becomes a significant public health problem in the world. During the early
detection of breast cancer, it is a very challenging task to classify accurately the benign …