Enhancing Dermatological Diagnosis Through Medical Image Analysis: How Effective Is YOLO11 Compared to Leading CNN Models? DOI Creative Commons

Rakib Ahammed Diptho,

Sarnali Basak

NDT, Год журнала: 2025, Номер 3(2), С. 11 - 11

Опубликована: Май 21, 2025

Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection clinical experience, frequently subjective, time-consuming, prone to mistakes. This investigation undertakes comparative analysis four state-of-the-art deep learning architectures, YOLO11, YOLOv8, VGG16, ResNet50, the context skin disease identification. study evaluates performance these models using pivotal metrics, building upon foundation YOLO paradigm, revolutionized spatial attention multi-scale representation. A properly selected collection 900 high-quality dermatological images with nine categories was used for investigation. Robustness generalizability were guaranteed by data augmentation hyperparameter adjustment. By varying benchmark balancing accuracy recall while limiting false positives negatives, YOLO11 obtained test 80.72%, precision 88.7%, 86.7%, an F1 score 87.0%. The expedition signifies promising trajectory development highly accurate detection models. Our not only highlights strengths weaknesses model but also underscores rapid techniques medical imaging.

Язык: Английский

Smart Plant Disease Diagnosis Using Multiple Deep Learning and Web Application Integration DOI Creative Commons

Ahmed M. S. Kheir,

Anis Koubâa,

Vinothkumar Kolluru

и другие.

Journal of Agriculture and Food Research, Год журнала: 2025, Номер 21, С. 101948 - 101948

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0
Editorial for the Special Issue “Advances in Medical Image Processing, Segmentation, and Classification” DOI Creative Commons
Wan Azani Mustafa, Hiam Alquran

Diagnostics, Год журнала: 2025, Номер 15(9), С. 1114 - 1114

Опубликована: Апрель 28, 2025

Medical data include various health indicators, such as physiological signals, images, and treatment histories, providing crucial insights into a patient's condition disease progression [...].

Язык: Английский

Процитировано

0
Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data DOI Creative Commons

Cristian Constantin Volovăț,

Călin Gheorghe Buzea,

Diana-Ioana Boboc

и другие.

Diagnostics, Год журнала: 2025, Номер 15(10), С. 1242 - 1242

Опубликована: Май 14, 2025

Background: Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are critical for guiding treatment strategies patient counseling. Methods: We propose novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers structured demographic data to predict overall survival time. Our dataset includes 148 from three institutions, featuring expert-annotated segmentations of enhancing tumors, necrosis, peritumoral edema. Two convolutional neural network backbones-ResNet-50 EfficientNet-B0-were fused fully connected layers processing tabular data. Models were trained using mean squared error loss evaluated through stratified cross-validation an independent held-out test set. Results: The model based on EfficientNet-B0 achieved state-of-the-art performance, attaining R2 score 0.970 absolute 3.05 days the Permutation feature importance highlighted edema-to-tumor ratio tumor volume as most informative predictors. Grad-CAM visualizations confirmed model's attention anatomically clinically relevant regions. Performance consistency across validation folds framework's robustness generalizability. Conclusions: This study demonstrates multimodal can deliver accurate, explainable, actionable predictions metastases. proposed offers promising foundation integration into real-world oncology workflows support personalized prognosis informed therapeutic decision-making.

Язык: Английский

Процитировано

0
Enhancing Dermatological Diagnosis Through Medical Image Analysis: How Effective Is YOLO11 Compared to Leading CNN Models? DOI Creative Commons

Rakib Ahammed Diptho,

Sarnali Basak

NDT, Год журнала: 2025, Номер 3(2), С. 11 - 11

Опубликована: Май 21, 2025

Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection clinical experience, frequently subjective, time-consuming, prone to mistakes. This investigation undertakes comparative analysis four state-of-the-art deep learning architectures, YOLO11, YOLOv8, VGG16, ResNet50, the context skin disease identification. study evaluates performance these models using pivotal metrics, building upon foundation YOLO paradigm, revolutionized spatial attention multi-scale representation. A properly selected collection 900 high-quality dermatological images with nine categories was used for investigation. Robustness generalizability were guaranteed by data augmentation hyperparameter adjustment. By varying benchmark balancing accuracy recall while limiting false positives negatives, YOLO11 obtained test 80.72%, precision 88.7%, 86.7%, an F1 score 87.0%. The expedition signifies promising trajectory development highly accurate detection models. Our not only highlights strengths weaknesses model but also underscores rapid techniques medical imaging.

Язык: Английский

Процитировано

0