Jaringan Syaraf Konvolusional Jobs

Temporal Lesion-Aware Dynamic Gated Multimodal Fusion Framework for DR and DME Analysis Using OLIVES and MMRDR Datasets Framework Overview The proposed framework introduces a Temporal Lesion-Aware Dynamic Gated Multimodal Fusion System for automated analysis of Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) using multimodal retinal imaging data. The framework combines fundus images, OCT scans, longitudinal retinal information, and optional clinical metadata to improve retinal disease classification, biomarker understanding, and temporal disease progression analysis. Unlike conventional multimodal retinal systems that use static feature fusion, the proposed framework employs a: Dynamic Gated Cross-Modal Fusion Mechanism that adaptively learns the importance of each retinal modal...

Brain disorder detection using Deep Learning Uses MRI brain scan images Detects multiple brain diseases automatically Early disease detection and diagnosis Multi-class classification system Diseases detected: Glioma tumor Pituitary tumor Meningioma tumor Alzheimer’s disease Healthy brain Uses Convolutional Neural Networks (CNN) Uses Transfer Learning techniques Models used: CNN VGG16 ResNet50 Dataset collected from Kaggle MRI datasets Combined brain tumor and Alzheimer datasets 1000 images used for training 250 images used for testing Image preprocessing and normalization Pixel values converted from 0–255 to 0–1 Feature extraction using convolution layers Pooling layers reduce computation Fully connected layers perform classification Softmax activation used for multi-clas...