DeepMedFuseX: Explainable DeepFake Cancer CT Scan Classification with Multi-Scale Attention and Transfer Learnin

DeepMedFuseX: Explainable DeepFake Cancer CT Scan Classification with Multi-Scale Attention and Transfer Learnin

Stať ve sborníku v databázi WoS či Scopus

This paper introduces DeepMedFuseX, a novel deep learning framework developed for the accurate classification of cancer computed tomography (CT) scans as either authentic or synthetic (deepfake). The proposed approach leverages transfer learning by utilizing a pre-trained ResNet-50 model, which has been fine-tuned on a specialized CT scan dataset. This model is further enhanced with the Convolutional Block Attention Module (CBAM). This multiscale attention mechanism recalibrates feature maps to capture intricate details in medical images both channel-wise and spatially. Emphasis is placed on the interpretability of the model through the integration of the Gradient-weighted Class Activation Mapping (Grad-CAM) framework, underscoring the critical importance of explainability in AI models, particularly within the medical domain. The DeepMedFuseX framework demonstrates significant improvements in classification accuracy and robustness on challenging datasets, providing a powerful tool for medical practitioners in combating the threat posed by deepfake medical images.

This paper introduces DeepMedFuseX, a novel deep learning framework developed for the accurate classification of cancer computed tomography (CT) scans as either authentic or synthetic (deepfake). The proposed approach leverages transfer learning by utilizing a pre-trained ResNet-50 model, which has been fine-tuned on a specialized CT scan dataset. This model is further enhanced with the Convolutional Block Attention Module (CBAM). This multiscale attention mechanism recalibrates feature maps to capture intricate details in medical images both channel-wise and spatially. Emphasis is placed on the interpretability of the model through the integration of the Gradient-weighted Class Activation Mapping (Grad-CAM) framework, underscoring the critical importance of explainability in AI models, particularly within the medical domain. The DeepMedFuseX framework demonstrates significant improvements in classification accuracy and robustness on challenging datasets, providing a powerful tool for medical practitioners in combating the threat posed by deepfake medical images.

Deep Learning;DeepFake;Transfer Learning;Multi-Scale Attention Mechanism;Explainable AI

Deep Learning;DeepFake;Transfer Learning;Multi-Scale Attention Mechanism;Explainable AI

GUPTA, S.; NANDI, T.; KAUSHAL, A.; DUTTA, M.; MEZINA, A.; BURGET, R.

2026

26.11.2024

Meloneras

978-3-8007-6544-7

ICUMT 2024; 16th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

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