The dataset comprised 2,058 images, with 1,370 categorized as benign and 688 as malignant. Their experimental results indicated that InceptionV3 yielded the highest classification accuracy, achieving ...

The platform, equipped with a camera, captures real-time images, runs inference, and displays classification results on serial port and TFT display. By training the model on six distinct classes, the ...

Artificial Intelligence (AI) plays a crucial role in the real-time classification of gastrointestinal (GI) tract images, significantly enhancing diagnostic accuracy and efficiency. By leveraging ...

Ablation studies showed that introducing the MIFA module and the PSFFN was critical to the model’s success. Combining these two elements led to notable improvements in performance across all datasets.

This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images ...

In contrast, MedVersa, the proposed solution, is a generalist learner that leverages a large language model as a learnable orchestrator. The unique architecture of MedVersa enables it to learn from ...

This dual capability enables MedVersa to train on diverse medical data across multiple modalities and tasks, resulting in general, shared representations. To support the development of MedVersa, the ...

Artificial intelligence (AI) and machine learning models are making progress at an unprecedented rate and have achieved remarkable performance in several specific tasks such as image classification, ...

This helps ensure enhanced visualization for image processing and AI-based decision-making support, such as for polyp detection and classification.The combination of LTTS expertise in medical-device ...