Deep learning for diabetic retinopathy assessments: a literature review
A Skouta, A Elmoufidi, S Jai-Andaloussi… - Multimedia Tools and …, 2023 - Springer
Diabetic retinopathy (DR) is the most important complication of diabetes. Early diagnosis by
performing retinal image analysis helps avoid visual loss or blindness. A computer-aided …
performing retinal image analysis helps avoid visual loss or blindness. A computer-aided …
Dilated CNN for abnormality detection in wireless capsule endoscopy images
N Goel, S Kaur, D Gunjan, SJ Mahapatra - Soft Computing, 2022 - Springer
Wireless capsule endoscopy is a non-invasive and painless procedure to examine the
gastrointestinal tract of human body, and an experienced clinician takes 2–3 hours for …
gastrointestinal tract of human body, and an experienced clinician takes 2–3 hours for …
Comparative analysis of diabetic retinopathy classification approaches using machine learning and deep learning techniques
Diabetic retinopathy (DR) is an eye disease caused due to excess of sugar in retinal blood
vessels and obstructs vision. Regular and timely diagnosis can prevent the severity of …
vessels and obstructs vision. Regular and timely diagnosis can prevent the severity of …
Tensor-rt-based transfer learning model for lung cancer classification
Cancer is a leading cause of death across the globe, in which lung cancer constitutes the
maximum mortality rate. Early diagnosis through computed tomography scan imaging helps …
maximum mortality rate. Early diagnosis through computed tomography scan imaging helps …
Knee osteoarthritis severity prediction using an attentive multi-scale deep convolutional neural network
Knee Osteoarthritis (OA) is a destructive joint disease identified by joint stiffness, pain, and
functional disability concerning millions of lives across the globe. It is generally assessed by …
functional disability concerning millions of lives across the globe. It is generally assessed by …
Automated system-based classification of lung cancer using machine learning
Lung malignant growth is the well-known reason for death identified due to cancer
worldwide. Therefore, to help the radiologist to detect it correctly, automated computer …
worldwide. Therefore, to help the radiologist to detect it correctly, automated computer …
EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection
Epilepsy is a common non-communicable, group of neurological disorders affecting more
than 50 million individuals worldwide. Researchers are working to automatically detect …
than 50 million individuals worldwide. Researchers are working to automatically detect …
Machine learning approaches in medical image analysis of PCOS
N Jan, A Makhdoomi, P Handa… - … Conference on Machine …, 2022 - ieeexplore.ieee.org
Polycystic Ovary Syndrome (PCOS) is a complex hormonal disorder which is associated
with diverse symptoms such as irregular menstrual cycles, obesity, acne issues, hirsutism …
with diverse symptoms such as irregular menstrual cycles, obesity, acne issues, hirsutism …
Transfer learning-based classification model for the Computed Tomography scan pulmonary images
In recent years, transfer learning has emerged as the most effective method for detecting
and classifying lung cancer. Early-stage lung cancer diagnosis using multiple slices of …
and classifying lung cancer. Early-stage lung cancer diagnosis using multiple slices of …
Automatic intestinal content classification using transfer learning architectures
Investigation of anomalies in capsule endoscopy (CE) is affected by an impairment of the
mucosal frames with bubbles, debris, intestinal fluid, foreign objects, and chyme (food) etc …
mucosal frames with bubbles, debris, intestinal fluid, foreign objects, and chyme (food) etc …