Machine learning detects mycosis fungoides from skin tissue images
Detail in Context: A Dual-Scale Machine Learning Framework for Mycosis Fungoides Detection
Computer Vision and Pattern Recognition
Summary
Mycosis fungoides (MF) is a rare and often misdiagnosed skin cancer that looks like harmless skin conditions early on. The authors propose a new computer method that looks at skin tissue images at two zoom levels to better spot MF. They combine detailed cell information with broader tissue structure and also use clinical data about patients. Their combined approach can help doctors more accurately detect MF early and classify its stage, which may improve treatment decisions.
What this means in practice
- •For dermatology clinics: Improve early screening of mycosis fungoides by automatically analyzing skin biopsy images at multiple magnifications alongside clinical data.
- •For medical imaging software developers: Develop enhanced diagnostic tools that fuse dual-scale image features with patient clinical information for skin lymphoma detection.$Commercial implications: Enables creation of commercial decision support systems for dermatologists diagnosing rare lymphomas using combined image and clinical data.
Authors
Mohamed Hazem, Tarek Waleed, Omar Khaled, Nada Omar, Mahmoud Raslan, Marwa Mohamed Fawzy, Aya Fahim, Rania M. Mogawer, Ahmed Mourad, Kariman Mansour, Muhammad Rushdi
Abstract
Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inflammatory dermatoses. Early and accurate diagnosis is critical for improving patient outcomes. In this paper, we propose a comprehensive diagnostic framework for automated MF detection that combines dual- scale histopathological image analysis with deep learning. To distinguish MF from other lymphoproliferative skin conditions, the proposed approach leverages a late-fusion ensemble of dual- magnification (10x and 20x) convolutional neural networks (CNNs), complemented by a random forest classifier trained on 16 clinical features. Experimental results on an expanded dataset of 6,267 images (4,306 MF; 1,961 Non-MF) across 463 patients demonstrate that strong detection performance is obtained by prioritizing higher-resolution cytological details (20x) within broader architectural context (10x). The image-based late-fusion model achieves an accuracy of 83.58% and a sensitivity of 89.13%, while the clinical random forest model achieves an accuracy of 96.6% and sensitivity of 93.8%, highlighting the po- tential of this multimodal framework as a robust clinical decision support system in dermatology. This framework addresses two distinct clinical objectives: an image-based dual-scale pipeline optimized for the early diagnostic screening of MF versus non- MF dermatoses, and a complementary clinical metadata model designed for the subsequent staging of confirmed MF cases (patch/plaque versus tumor)