Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm
In the last decade, there has been a significant increase in medical cases involving brain
tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people …
tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people …
MobileNetV1-based deep learning model for accurate brain tumor classification
Brain tumors are among the most dangerous diseases that lead to mortality after a period of
time from injury. Therefore, physicians and healthcare professionals are advised to make an …
time from injury. Therefore, physicians and healthcare professionals are advised to make an …
PoxNet22: A fine-tuned model for the classification of monkeypox disease using transfer learning
Officials in the field of public health are concerned about a new monkeypox outbreak, even
though the world is now experiencing an epidemic of COVID-19. Similar to variola, cowpox …
though the world is now experiencing an epidemic of COVID-19. Similar to variola, cowpox …
Deep learning models performance evaluations for remote sensed image classification
Deep learning-based land cover and land use (LCLU) classification systems are a
significant aspiration for remote sensing communities. In nature, remote sensing images …
significant aspiration for remote sensing communities. In nature, remote sensing images …
A lightweight deep learning based microwave brain image network model for brain tumor classification using reconstructed microwave brain (rmb) images
Computerized brain tumor classification from the reconstructed microwave brain (RMB)
images is important for the examination and observation of the development of brain …
images is important for the examination and observation of the development of brain …
TumorGANet: A transfer learning and generative adversarial network-based data augmentation model for brain tumor classification
Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant
challenges due to the complexities of segmentation and the variability in tumor …
challenges due to the complexities of segmentation and the variability in tumor …
BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification
A significant issue in computer-aided diagnosis (CAD) for medical applications is brain
tumor classification. Radiologists could reliably detect tumors using machine learning …
tumor classification. Radiologists could reliably detect tumors using machine learning …
A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms
Breast cancer is the second most frequent cancer worldwide, following lung cancer and the
fifth leading cause of cancer death and a major cause of cancer death among women. In …
fifth leading cause of cancer death and a major cause of cancer death among women. In …
Optimized deep learning model for comprehensive medical image analysis across multiple modalities
SUR Khan, S Asif, M Zhao, W Zou, Y Li, X Li - Neurocomputing, 2025 - Elsevier
This study presents a novel amalgamated model for the diagnosis of multiple medical
conditions using various imaging modalities, including Chest X-ray, MRI, and endoscopic …
conditions using various imaging modalities, including Chest X-ray, MRI, and endoscopic …
Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection
Deep learning models, such as convolutional neural network (CNN), are popular now a day
to solve various complex problems in medical and other fields, such as image classification …
to solve various complex problems in medical and other fields, such as image classification …