Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms
Objective: Mammogram-based automatic breast cancer detection has a primary role in
accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is …
accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is …
PSOWNNs‐CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods
A Nomani, Y Ansari, MH Nasirpour… - Computational …, 2022 - Wiley Online Library
Early diagnosis of breast cancer is an important component of breast cancer therapy. A
variety of diagnostic platforms can provide valuable information regarding breast cancer …
variety of diagnostic platforms can provide valuable information regarding breast cancer …
A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA)
These days, the ratio of cancer diseases among patients has been growing day by day.
Recently, many cancer cases have been reported in different clinical hospitals. Many …
Recently, many cancer cases have been reported in different clinical hospitals. Many …
A diabetes monitoring system and health-medical service composition model in cloud environment
Diabetes is a common chronic illness or absence of sugar in the blood. The early detection
of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud …
of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud …
Cumulative Major Advances in Particle Swarm Optimization from 2018 to the Present: Variants, Analysis and Applications
Abstract Particle Swarm Optimization (PSO) is a key tool in Artificial Intelligence, is well-
known to the public for its effectiveness in addressing complex and diverse problems. It …
known to the public for its effectiveness in addressing complex and diverse problems. It …
An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection
Early detection and diagnosis are the key factors in decreasing the breast cancer mortality
rate in medical image analysis. A randomized learning technique called extreme learning …
rate in medical image analysis. A randomized learning technique called extreme learning …
A novel breast cancer detection system using SDM-WHO-RNN classifier with LS-CED segmentation
GR Paul, J Preethi - Expert Systems with Applications, 2024 - Elsevier
Breast cancer (BC) is caused by the abnormal and rapid growth of breast cells. Accurate
diagnosis of BC at an early stage could minimize the mortality related to this disease …
diagnosis of BC at an early stage could minimize the mortality related to this disease …
Applying dual models on optimized LSTM with U-net segmentation for breast cancer diagnosis using mammogram images
J Sivamurugan, G Sureshkumar - Artificial Intelligence in Medicine, 2023 - Elsevier
Background of the study Breast cancer is the most fatal disease that widely affects women.
When the cancerous lumps grow from the cells of the breast, it causes breast cancer. Self …
When the cancerous lumps grow from the cells of the breast, it causes breast cancer. Self …
Particle swarm optimization-based extreme learning machine for covid-19 detection
Abstract COVID-19 (coronavirus disease 2019) is an ongoing global pandemic caused by
severe acute respiratory syndrome coronavirus 2. Recently, it has been demonstrated that …
severe acute respiratory syndrome coronavirus 2. Recently, it has been demonstrated that …
An evolutionary supply chain management service model based on deep learning features for automated glaucoma detection using fundus images
Glaucoma, a multifaceted eye condition, poses a high risk of vision impairment. Initially, most
automated approaches segment the primary system and assess the clinical measurements …
automated approaches segment the primary system and assess the clinical measurements …