Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans

T Saeed, MA Khan, A Hamza, M Shabaz… - Journal of Neuroscience …, 2024‏ - Elsevier
The prevalence of brain tumor disorders is currently a global issue. In general, radiography,
which includes a large number of images, is an efficient method for diagnosing these life …

Facial classification for autism spectrum disorder

M Fahaad Almufareh, S Tehsin… - Journal of Disability …, 2024‏ - scienceopen.com
Autism spectrum disorder (ASD) is a mental condition that affects people's learning,
communication, and expression in their daily lives. ASD usually makes it difficult to socialize …

An edge computing-based factor-aware novel framework for early detection and classification of melanoma disease through a customized VGG16 architecture with …

MF Almufareh - IEEE Access, 2024‏ - ieeexplore.ieee.org
Melanoma is dangerous skin cancer disease with high malignancy potential, necessitates
advanced detection methods for improved patient outcomes. This study proposes a novel …

YOLOv7 for brain tumour detection using morphological transfer learning model

SK Pandey, AK Bhandari - Neural Computing and Applications, 2024‏ - Springer
An accurate diagnosis of a brain tumour in its early stages is required to improve the
possibility of survival for cancer patients. Due to the structural complexity of the brain, it has …

A systematic review of trending technologies in non-invasive automatic brain tumor detection

Jyoti, A Kumar - Multimedia Tools and Applications, 2024‏ - Springer
This manuscript provides a detailed review of state-of-the-art techniques for identifying brain
tumors using magnetic resonance imaging (MRI) analysis, focusing on automatic brain …

Automated image clarity detection for the improvement of colposcopy imaging with multiple devices

L Ekem, E Skerrett, MJ Huchko… - … Signal Processing and …, 2025‏ - Elsevier
The proportion of women dying from cervical cancer in middle-and low-income countries is
over 60%, twice that of their high-income counterparts. A primary screening strategy to …

Brain tumor classification using mobilenet

MP Kumar, D Hasmitha, B Usha… - 2024 International …, 2024‏ - ieeexplore.ieee.org
One of the deadliest diseases is a brain tumor, which develops when brain tissue inside the
skull grows suddenly and uncontrollably. Brain tumors are a significant health concern …

C-SAN: Convolutional stacked autoencoder network for brain tumor detection using MRI

R Gayathiri, S Santhanam - Biomedical Signal Processing and Control, 2025‏ - Elsevier
Abstract Magnetic Resonance Imaging (MRI) is a medical imaging technique that uses
strong magnetic fields and radio waves to generate detailed images of the brain and other …

[HTML][HTML] A lightweight attention-driven YOLOv5m model for improved brain tumor detection

S Muksimova, S Umirzakova, S Mardieva… - Computers in Biology …, 2025‏ - Elsevier
Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases,
significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of …

Node-Based Graph Convolutional Network with SLIC Method for Breast Cancer Ultrasound Images Classification

K Trang, FF Ting, BQ Vuong, CM Ting - IEEE Access, 2024‏ - ieeexplore.ieee.org
This research presents a novel node-based Graph Convolutional Network (GCN) approach
for the classification of breast cancer from ultrasound images. Utilizing the Simple Linear …