# RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis

arXiv: https://arxiv.org/abs/2410.01216 · HTML: https://mlsanity.com/paper/2410.01216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study](https://mlsanity.com/paper/2207.03342/) (2022)
- [Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder](https://mlsanity.com/paper/2108.11720/) (2021)
- [Monkeypox virus detection using pre-trained deep learning-based approaches](https://mlsanity.com/paper/2209.04444/) (2022)
- [Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16](https://mlsanity.com/paper/2206.01862/) (2022)
- [Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN](https://mlsanity.com/paper/2211.16571/) (2022)
- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection](https://mlsanity.com/paper/2311.10754/) (2023)
- [Malware Classification Using Deep Boosted Learning](https://mlsanity.com/paper/2107.04008/) (2021)
- [Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening](https://mlsanity.com/paper/1807.03120/) (2018)
- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [A New Channel Boosted Convolutional Neural Network using Transfer Learning](https://mlsanity.com/paper/1804.08528/) (2018)
- [Automatic Infectious Disease Classification Analysis with Concept Discovery](https://mlsanity.com/paper/2209.02415/) (2022)

## Built on by (most cited first, 4 of 4)

- [RS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detection](https://mlsanity.com/paper/2511.15476/) (2025)
- [Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction](https://mlsanity.com/paper/2508.05210/) (2025)
- [RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Images](https://mlsanity.com/paper/2601.01835/) (2026)
- [DenseSwinV2: Channel Attentive Dual Branch CNN Transformer Learning for Cassava Leaf Disease Classification](https://mlsanity.com/paper/2603.25935/) (2026)

## Abstract (arXiv)

Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets include data scarcity, texture, contrast, inter-intra class variability, and similarities with other skin infectious diseases. In this regard, a novel hybrid approach is proposed that integrates the learning capacity of Residual Learning and Spatial Exploitation Convolutional Neural Network (CNN) with a customized Swin Transformer (RS-FME-SwinT) to capture multi-scale global and local correlated features for MPox diagnosis. The proposed RS-FME-SwinT technique employs a transfer learning-based feature map enhancement (FME) technique, integrating the customized SwinT for global information capture, residual blocks for texture extraction, and spatial blocks for local contrast variations. Moreover, incorporating new inverse residual blocks within the proposed SwinT effectively captures local patterns and mitigates vanishing gradients. The proposed RS-FME-SwinT has strong learning potential of diverse features that systematically reduce intra-class MPox variation and enable precise discrimination from other skin diseases. Finally, the proposed RS-FME-SwinT is a holdout cross-validated on a diverse MPox dataset and achieved outperformance on state-of-the-art CNNs and ViTs. The proposed RS-FME-SwinT demonstrates commendable results of an accuracy of 97.80%, sensitivity of 96.82%, precision of 98.06%, and an F-score of 97.44% in MPox detection. The RS-FME-SwinT could be a valuable tool for healthcare practitioners, enabling prompt and accurate MPox diagnosis and contributing significantly to mitigation efforts.