CoAtNeXt:An Attention-Enhanced ConvNeXtV2-Transformer Hybrid Model for Gastric Tissue Classification

Background and objective Early diagnosis of gastric diseases is crucial to prevent fatal outcomes. Although histopathologic examination remains the diagnostic gold standard, it is performed entirely manually, making evaluations labor-intensive and prone to variability among pathologists. Critical findings may be missed, and lack of standard procedures reduces consistency. These limitations highlight the need for automated, reliable, and efficient methods for gastric tissue analysis. Methods In this study, a novel hybrid model named CoAtNeXt was proposed for the classification of gastric tissue images. The model is built upon the CoAtNet architecture by replacing its MBConv layers with enhanced ConvNeXtV2 blocks. Additionally, the Convolutional Block Attention Module (CBAM) is integrated to improve local feature extraction through channel and spatial attention mechanisms. The architecture was scaled to achieve a balance between computational efficiency and classification performance. CoAtNeXt was evaluated on two publicly available datasets, HMU-GC-HE-30K for eight-class classification and GasHisSDB for binary classification, and was compared against 10 Convolutional Neural Networks (CNNs) and ten Vision Transformer (ViT) models. Results CoAtNeXt achieved 96.47% accuracy, 96.60% precision, 96.47% recall, 96.45% F1 score, and 99.89% AUC on HMU-GC-HE-30K. On GasHisSDB, it reached 98.29% accuracy, 98.07% precision, 98.41% recall, 98.23% F1 score, and 99.90% AUC. It outperformed all CNN and ViT models tested and surpassed previous studies in the literature. Conclusion Experimental results show that CoAtNeXt is a robust architecture for histopathological classification of gastric tissue images, providing performance on binary and multiclass. Its highlights its potential to assist pathologists by enhancing diagnostic accuracy and reducing workload.

Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis

Hypertensive retinopathy (HR) is a severe eye disease that may cause permanent vision loss if not diagnosed early. Traditional diagnostic methods are time-consuming and subjective, highlighting the need for an automated, reliable system. Existing studies often use a single Deep Learning (DL) model, struggling to distinguish HR stages. This study introduces a three-stage approach to enhance HR diagnosis accuracy. Initially, 14 CNN models were tested, identifying DenseNet169, MobileNet, and ResNet152 as the most effective. DenseNet169 achieved 87.73% accuracy, 87.75% precision, 87.73% recall, 87.67% F1-score, and 0.8359 Cohen's Kappa. MobileNet followed with 86.40% accuracy, 86.60% precision, 86.40% recall, 86.31% F1-score, and 0.8180 Cohen's Kappa. ResNet152 ranked third with 85.87% accuracy, 86.01% precision, 85.87% recall, 85.83% F1-score, and 0.8188 Cohen's Kappa. In the second stage, deep features from these models were fused and classified using Machine Learning (ML) algorithms (SVM, RF, XGBoost). SVM (sigmoid kernel) performed best with 92.00% accuracy, 91.93% precision, 92.00% recall, 91.91% F1-score, and 0.8930 Cohen's Kappa. The third stage applied meta-heuristic optimization (GA, ABC, PSO, HHO) for feature selection. HHO yielded 94.66% accuracy, precision, and recall, 94.64% F1-score, and 0.9286 Cohen's Kappa. The proposed approach surpassed single CNN models and previous studies in HR diagnosis accuracy and generalization.

MaxGlaViT: A novel lightweight vision transformer-based approach for early diagnosis of glaucoma stages from fundus images

Glaucoma is a prevalent eye disease that progresses silently without symptoms. If not detected and treated early, it can cause permanent vision loss. Computer-assisted diagnosis systems play a crucial role in timely and efficient identification. This study introduces MaxGlaViT, a lightweight model based on the restructured Multi-Axis Vision Transformer (MaxViT) for early glaucoma detection. First, MaxViT was scaled to optimize block and channel numbers, resulting in a lighter architecture. Second, the stem was enhanced by adding attention mechanisms (CBAM, ECA, SE) after convolution layers to improve feature learning. Third, MBConv structures in MaxViT blocks were replaced by advanced DL blocks (ConvNeXt, ConvNeXtV2, InceptionNeXt). The model was evaluated using the HDV1 dataset, containing fundus images of different glaucoma stages. Additionally, 40 CNN and 40 ViT models were tested on HDV1 to validate MaxGlaViT's efficiency. Among CNN models, EfficientB6 achieved the highest accuracy (84.91%), while among ViT models, MaxViT-Tiny performed best (86.42%). The scaled MaxViT reached 87.93% accuracy. Adding ECA to the stem block increased accuracy to 89.01%. Replacing MBConv with ConvNeXtV2 further improved it to 89.87%. Finally, integrating ECA in the stem and ConvNeXtV2 in MaxViT blocks resulted in 92.03% accuracy. Testing 80 DL models for glaucoma stage classification, this study presents a comprehensive and comparative analysis. MaxGlaViT outperforms experimental and state-of-the-art models, achieving 92.03% accuracy, 92.33% precision, 92.03% recall, 92.13% f1-score, and 87.12% Cohen's kappa score.

Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines

Detecting machine malfunctions at an early stage is crucial for reducing interruptions in operational processes within industrial settings. Recently, the deep learning approach has started to be preferred for the detection of failures in machines. Deep learning provides an effective solution in fault detection processes thanks to automatic feature extraction. In this study, a deep learning-based system was designed to analyze the sound signals produced by industrial machines. Acoustic sound signals were converted into Mel spectrograms. For the purpose of classifying spectrogram images, the DenseNet-169 model, a deep learning architecture recognized for its effectiveness in image classification tasks, was used. The model was trained using the transfer learning method on the MIMII dataset including sounds from four types of industrial machines. The results showed that the proposed method reached an accuracy rate varying between 97.17% and 99.87% at different Sound Noise Rate levels.