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

Summary

The use of a curated collection of 2,280 skin-lesion images produced a standardized binary deep-learning workflow to classify the presence of mpox in each image. A custom and InceptionV3, ResNetV2, ResNet50, DenseNet121, and a hybrid CNN-transformer model were compared in the context of a common preprocessing and training pipeline.

Abstract

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.

Introduction

Automated and fast detection of mpox in medical cases is clinically significant since the disease can be similar to a variety of other vesicular or pustular eruptions at the initial visual evaluation. Clinicians in practice do not pay much attention to the morphology of lesions alone, but they combine pattern, distribution, history, exposure risk, epidemiological context, and laboratory confirmation¹. Nonetheless, computational tools use images and can also perform a helpful triage procedure in environments where skilled dermatological review is constrained or delayed. It is especially applicable when it comes to outbreak tracking, teledermatol....

Protocol

NOTE: Use the curated binary mpox image dataset described in the manuscript for the main experiment. The dataset contains 2,280 skin-lesion images, including 1,020 mpox images and 1,260 images labeled as Other. Use secondary image analysis only; do not perform direct patient recruitment, clinical intervention, animal experimentation, or generation of new biological specimens as part of this workflow.

1. Obtain and curate the dataset

1. Define a binary labeling scheme with two classes: mpox and Other.
2. Assign visually similar non-mpox lesions to the Other class.
3. Screen all images for readability, ....

Results

Comparison beyond headline accuracy

These results show that comparisons of models in medical image analysis should not only focus on headline accuracy, but also on the behavior of the architectures with regard to a number of metrics and different experimental settings. Throughout the experiments, the models were not similarly behaving: the simple Sequential CNN was extremely high-performing on the internal validation split, InceptionV3 overfitted, ResNetV2 was not sufficiently.......

Discussion

The key result of the study is that binary classification of mpox versus visually similar lesions, named automated classification, is possible with the curated dataset that was used in this study, but such a success largely relies on the interpretation of evidence²⁷. Separating the experiments appropriately, the manuscript presents evidence that both conventional and proposed hybrid architectures can be used to perform strong performance²⁸. It is thus inappropriate to view .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Dataset	Curated mpox image dataset; 2,280 images for the main binary benchmark (1,020 mpox and 1,260 Other)	Primary training and internal validation source
Task definition	Binary classification for the core study; supplementary multiclass and augmented summaries retained separately	Ensures consistent interpretation of reported metrics
Programming environment	Python-based notebook workflow	Data handling, model training, and plotting
Deep-learning framework	TensorFlow / Keras implementation reported in the submitted code	Model development and optimization
Image utilities	ImageDataGenerator with rescaling and augmentation; plotting libraries for training curves	Preprocessing, augmentation, and visual reporting
Baseline architectures	Sequential CNN, InceptionV3, ResNetV2, ResNet50, DenseNet121	Comparative benchmarking
Proposed architecture	Hybrid CNN-transformer classifier	Primary methodological contribution
Training settings	Input size 150 x 150; batch size 32; Adam optimizer; binary cross-entropy; 15 epochs reported for supplied baseline code	Reproducibility of the core workflow
Compute environment	GPU-enabled computational system; hardware details generalized in the narrative	Model acceleration during training