Seeing Beyond: Advanced Image and Thermal Analysis for Early Detection of Diabetic Retinopathy and Diabetes

Biomedical & Pharmacology Journal, Journal Year: 2025, Volume and Issue: 18(December Spl Edition), P. 191 - 202

Published: Jan. 20, 2025

Diabetes mellitus (DM) is a chronic metabolic disorder condition that requires continuous monitoring and early detection to prevent serious complications such as diabetic retinopathy (DR) foot (DF) disease. In recent years, medical imaging technologies coupled with machine learning techniques have made progress in the automated of DM-related using retina or images. This article proposes novel Ens-DRDF model integrates ulcers advanced image processing techniques. The process involves removing optic disc blood vessels, followed by feature extraction, segmentation, classification. Fuzzy clustering aids lesion differentiation, enhancing quality for improved DR diagnosis.

Language: Английский

---

LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2787 - e2787

Published: March 31, 2025

Segmenting brain tumors is a critical task in medical imaging that relies on advanced deep-learning methods. However, effectively handling complex tumor regions requires more comprehensive and strategies to overcome challenges such as computational complexity, the gradient vanishing problem, variations size visual impact. To these challenges, this research presents novel computationally efficient method termed lightweight Inception U-Net (LIU-Net) for accurate segmentation task. LIU-Net balances model complexity load provide consistent performance uses blocks capture features at different scales, which makes it relatively lightweight. Its capability efficiently precisely segment tumors, especially challenging-to-detect regions, distinguishes from existing models. This Inception-style convolutional block assists capturing multiscale while preserving spatial information. Moreover, proposed utilizes combination of Dice loss Focal handle class imbalance issue. The was evaluated benchmark BraTS 2021 dataset, where generates remarkable outcomes with score 0.8121 enhancing (ET) region, 0.8856 whole (WT) 0.8444 core (TC) region test set. evaluate robustness architecture, cross-validated an external cohort 2020 dataset. obtained 0.8646 ET 0.9027 WT 0.9092 TC These results highlight effectiveness integrating into making promising candidate image segmentation.

Language: Английский

---

PHOTODIAGNOSIS WITH DEEP LEARNING: A GAN AND AUTOENCODER-BASED APPROACH FOR DIABETIC RETINOPATHY DETECTION

Photodiagnosis and Photodynamic Therapy, Journal Year: 2025, Volume and Issue: unknown, P. 104552 - 104552

Published: March 1, 2025

Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide, necessitating early detection accurate diagnosis. This study proposes novel framework integrating Generative Adversarial Networks (GANs) for data augmentation, denoising autoencoders noise reduction, transfer learning with EfficientNetB0 to enhance the performance DR classification models. GANs were employed generate high-quality synthetic retinal images, effectively addressing class imbalance enriching training dataset. Denoising further improved image quality by reducing eliminating common artifacts such as speckle noise, motion blur, illumination inconsistencies, providing clean consistent inputs model. was fine-tuned on augmented denoised The achieved exceptional metrics, including 99.00% accuracy, recall, specificity, surpassing state-of-the-art methods. custom-curated OCT dataset featuring high-resolution clinically relevant challenges limited annotated noisy inputs. Unlike existing studies, our work uniquely integrates GANs, autoencoders, EfficientNetB0, demonstrating robustness, scalability, clinical potential proposed framework. Future directions include interpretability tools adoption exploring additional imaging modalities improve generalizability. highlights transformative deep in critical diabetic

Language: Английский

---

Enhancing skin lesion classification: a CNN approach with human baseline comparison

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2795 - e2795

Published: April 15, 2025

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN while replacing low-confidence outputs expert assessments to enhance diagnostic accuracy. A model utilizing EfficientNetB3 backbone is trained datasets from ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges evaluated a 150-image test set. model’s are compared against 69 experienced medical professionals. Performance assessed using receiver operating characteristic (ROC) curves area under curve (AUC) metrics, alongside analysis resource costs. baseline achieves AUC 0.822, slightly below performance experts. However, improves true positive rate 0.782 reduces false 0.182, delivering better minimal involvement. offers scalable, resource-efficient solution address variability in image analysis, effectively harnessing complementary strengths humans CNNs.

Language: Английский

---

Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model

Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 26, 2025

Language: Английский

---

Hybrid Bayesian Deep Learning for Explainable Breast Cancer Diagnosis in Telemedicine: Integrating Multi-Modal Data

Published: Jan. 1, 2025

Language: Английский

---