Innovative Hybrid CNN-Transformer Deep Learning Models For The Automated Diagnosis Of Monkeypox From Medical Images

The visual appearance of monkey pox skin lesions remains poorly established due to their similarity to other vesicular and pustular skin diseases. This paper tested the hypothesis that a hybrid deep-learning model, integrated with convolutional and transformer-based feature encoders, can automatically classify mpox images within a unified experimental pipeline. To ensure consistency across models and reports, the main analysis was conducted as a binary task: mpox vs other conditions with the same or similar presentation. These benchmark data consisted of 2,280 images: 1,020 mpox and 1,260 non-mpox lesion images. The images were downsampled to a standard input size of 150 × 150 pixels, and the values were in the range [0, 1]. During training, the images were augmented with rotation, translation, shear, zoom, and horizontal flipping. A custom Sequential CNN, InceptionV3, ResNetV2, ResNet50, DenseNet121, and a proposed hybrid CNN-transformer architecture were compared. Every baseline model was trained for 15 epochs using the Adam optimizer and binary cross-entropy loss. Training and validation accuracy, loss, precision, recall, F1-score, and area under the receiver operating characteristic curve were measured to assess performance, where possible. The best reported internal validation accuracy was observed with the Sequential CNN, and the hybrid model achieved a precision of 98.50, a recall of 98.50, and an F1-score of 98.58, yielding a balanced discriminative profile. InceptionV3 shows signs of overfitting, and ResNetV2 did not provide sufficient validation data to support a robust generalization test. On the whole, the hybrid model can be viewed as a viable and balanced strategy and not necessarily better than all baselines.