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A. S.

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Conference Aug 2026

Improving Thyroid Cancer Diagnosis and Risk Stratification with VGGNet-Driven Deep Learning from Multimodal Imaging

Recent developments in deep learning have demonstrated potential in improving the analysis of medical images in diagnosing cancer. This paper tackles the problem of precise diagnosis of thyroid cancer and stratification of risk based on multimodal imaging data. We suggest a deep-learning structure using the VGGNet fram...

A. S. · 0 citations
Conference Aug 2026

Comparative Evaluation of Advanced Deep Learning Approaches for Melanoma Detection in Dermoscopic Images Toward Supporting Clinical Diagnosis

Melanoma is a type of skin cancer that is one of the most aggressive, and its diagnosis requires quick and accurate identification to increase patient survival. This paper draws a comparative analysis of advanced deep learning systems, namely, VGG and traditional CNN systems, used in automatic melanoma detection in der...

A. S. · 0 citations
Conference Aug 2026

Accurate Detection and Prognostic Assessment of Colon Cancer Using an R-MobileNet-Driven Deep Learning Model with Clinical Structured Data

Proper diagnosis and prognosis of colon cancer is essential to enhancing patient outcomes and informing individual treatment plans. This paper has formulated a deep learning model that is powered by the R-MobileNet design to perform effective colon cancer classification through clinical structured data. The model relie...

A. S. · 0 citations
Conference Jul 2026

Automated Epilepsy Diagnosis using AI-based ResNet152 Deep Learning for Early Detection and Improved Patient Health Outcomes

Epilepsy is a long-lasting neurological condition that is characterized by repeated seizures and affects millions of people across the world. Traditional methods of diagnosis that are based on manual interpretation of the clinical examinations and electroencephalogram (EEG) are frequently time-consuming and subjective....

A. S. · 0 citations

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