Aug 2026· Bulletin of Electrical Engineering and Informatics· 0 citations· 27 references
TL;DR
The results indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation.
Abstract
Image classification is a key application of computer vision with direct relevance to medical diagnostics, autonomous vehicles, and remote sensing. This paper discusses the use of an adaptive learning convolutional neural network (AL-CNN) for image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation. The AL-CNN architecture integrates convolutional, pooling, and fully connected layers. The model was systematically trained on a subset of the dataset and subsequently tested on an independent validation subset to evaluate its efficiency and generalization capability. In addition, optimization techniques such as data augmentation, dropout, and advanced activation functions were employed to further enhance model performance. The results, based on accuracy metrics, indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification. This study demonstrates the potential of the AL-CNN approach to address various complexities in image classification, thereby enabling further innovation in this domain.
A comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures is presented, showing that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency.
Riyaz Mohammed· International Journal of App...· 0 citations
A lightweight CNN architecture that achieves competitive performance without relying on pretrained models or transfer learning approaches, making it suitable for deployment on resource-constrained devices and balanced classification performance across all classes is developed.
This study proposes a novel hybrid deep learning framework, ViT-CNN-ATS, designed for accurate classification and segmentation of breast cancer in ultrasound images. The model integrates the global contextual learning of the Vision Transformer (ViT) with the powerful local feature extraction capabilities of Convolutional Neural Networks (CNN), further refined by Attention Token Selection (ATS) for enhanced focus on the most informative regions. To overcome the inherent challenges of ultrasound imaging such as low contrast, speckle noise, and variability in tumor appearance comprehensive preprocessing techniques including contrast enhancement and noise reduction are employed. A masked ultrasound image dataset, organized into benign, malignant, and normal classes, serves as the foundation for training and evaluation. Data augmentation strategies are incorporated to improve model robustness and generalization. The proposed hybrid model demonstrates significant improvements in classification accuracy, Dice Similarity Coefficient, and Intersection over Union (IoU) scores when compared to baseline models using only CNN or ViT. These results underscore the potential of ViT-CNN-ATS as a reliable, AI-assisted diagnostic tool to support radiologists in early and precise breast cancer detection using ultrasound imaging.
Lakshmi S, M T Somashekara, Asha C. S· International journal of com...· 0 citations
Convolutional Neural Networks (CNNs) have exposed robust results for classifying images in data analytics. On the other hand, the effectiveness of these models is often limited by their substantial computational complexities, particularly when dealing with features represented in complex mathematical environments, multi class image classification, and especially when dealing with complex nonlinear relationships between features, which need to be addressed for transparent decision-making processes. This study developed a Quantum-Enhanced Convolutional Neural Network (Q-CNN) model, which incorporates traditional convolution-based feature extraction, Quantum Feature Encoding, and quantum processing. The developed model applies a quantum encoding mechanism to map classical features to quantum states for alternative feature representations and classification purposes. Experimental results show that the Q-CNN outperformed the standard CNN, reaching 97% training and validation accuracy compared to 94%, and lowering training and validation losses from 17% and 18% to 9% and 11%, respectively. Furthermore, precision, recall, and F1-score were increased from 90%, 88%, and 89% to 92%, 94%, and 93%, respectively. These findings demonstrate the potential effectiveness of quantum feature encoding for image classification tasks and provide a structured framework for enriched feature representation in convolution-based image processing models.
S. H. Niranjala, Kazem Chamran, Mustafa Mowafak Alobaedy· International Journal of Sof...· 0 citations
The results show that larger models and larger pretraining datasets do not automatically lead to better downstream performance, and transfer effectiveness in medical imaging is driven primarily by architectural inductive biases, pretraining strategy, and domain relevance.
Dina A. Elkholy, Mohamed S. Shehata, John W. Braun· Journal of imaging informati...· 0 citations
