Results 61 to 70 of about 12,736 (188)

Automated Tuberculosis Detection in Chest Radiographs: A Hybrid Deep Learning Framework for Clinical Decision Support

Applied AI Letters, Volume 7, Issue 2, June 2026.
A hybrid deep learning framework integrating VGG16, ResNet50, and DenseNet121 is proposed for automated tuberculosis detection from chest X‐ray images. Feature‐level fusion enhances robustness and generalization, achieving 97.4% accuracy across multiple public datasets, supporting reliable clinical decision‐making in resource‐limited healthcare ...
Md. Tahmid Hossain, Hrittik Mural, Monowar Wadud Hridoy   +2 more
wiley   +1 more source

Advancing Canning Quality in Common Beans: An Integrated Farm‐to‐Can Framework Combining Breeding, Processing, and Artificial Intelligence

Legume Science, Volume 8, Issue 2, June 2026.
ABSTRACT Common beans (Phaseolus vulgaris L.) are essential raw material for the canning industry. This article reviews recent advances in assessing canning quality and the integration of artificial intelligence (AI) into breeding methodologies aimed at developing genotypes with superior yield and canning‐quality traits.
Arash Ghaitaranpour, Biniam Kebede, Mohsen Yoosefzadeh‐Najafabadi   +2 more
wiley   +1 more source

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, Volume 89, Issue 5, Page 791-814, May 2026.
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source

Comparative Analysis of VGG16 and ResNet50 Model Performence in Cardiac ECG Image Classification

Journal of Applied Informatics and Computing
This study systematically evaluates and compares the effectiveness of two deep learning architectures, VGG16 and ResNet50, in automating electrocardiogram (ECG) image classification for cardiac condition diagnosis.
Hanif Rizaqi, Imam Tahyudin
doaj   +1 more source

Mitigating Site‐Specific Data Dependency in Image‐Based Water Level Estimation Using Vision Foundation Models

Water Resources Research, Volume 62, Issue 5, May 2026.
Abstract The increasing frequency and severity of fluvial flooding underscores the urgent need for accurate and timely river water level monitoring. Visual gauges based on river cameras offer a cost‐effective means for water level observation. However, conventional end‐to‐end deep learning regression models that infer water levels from images typically
Shiyuan Hu, Ze Wang, Xin Fan, Chi Zhang
wiley   +1 more source

On the Optimal Selection of Mel‐Frequency Cepstral Coefficients for Voice Deepfake Detection

Expert Systems, Volume 43, Issue 5, May 2026.
ABSTRACT The continuous evolution of techniques for generating manipulated audio, known as voice deepfakes, and the widespread availability of tools that produce convincing forgeries have created an urgent need for reliable detection methods. This work considers the dimensionality of Mel‐Frequency Cepstral Coefficients (MFCCs) as a core design variable
Sergio A. Falcón‐López, Llanos Tobarra, Antonio Robles‐Gómez, Rafael Pastor‐Vargas   +3 more
wiley   +1 more source

A Comparison of MobileNetV2 and VGG16 Architectures with Transfer Learning for Multi-Class Image-Based Waste Classification

Journal of Applied Informatics and Computing
Effective waste management represents a global challenge with significant environmental and public health impacts. Despite existing waste classification systems achieving high accuracy rates, a critical research gap exists in determining optimal CNN ...
Raffa Adhi Kumala, Christy Atika Sari, Eko Hari Rachmawanto   +2 more
doaj   +1 more source

Optimized Deep Learning‐Based Cabbage Stem Detection and Depth Classification

Journal of Food Process Engineering, Volume 49, Issue 5, May 2026.
A deep learning‐based approach using YOLOv5 enables accurate cabbage stem detection and depth classification, supporting efficient and automated cabbage processing applications. ABSTRACT In food industry, removing the nonedible parts causes reduction of production efficiency due to its heavily labor‐intensive process with a lot of loss of the edible ...
Tae Hyong Kim, Roh Jonghyeok, Ah‐Na Kim, Ki Hyun Kwon   +3 more
wiley   +1 more source

Skin Lesion Classification: A Deep Learning Approach with Local Interpretable Model-Agnostic Explanations (LIME) for Explainable Artificial Intelligence (XAI)

JOIV: International Journal on Informatics Visualization
The classification of skin cancer is crucial as the chance of survival increases significantly with timely and accurate treatment. Convolution Neural Networks (CNNs) have proven effective in classifying skin cancer. However, CNN models are often regarded
Sin Yi Hong, Lih Poh Lin
doaj   +1 more source

Classification of Tulungagung Batik Images in Comparison of Convolution Neural Network and Vision Transformer Algorithms: Author's Country: Indonesia

Buana Information Technology and Computer Sciences
Batik is a significant Indonesian cultural heritage with a vast diversity of motifs, making manual classification a challenging task. This research provides a comparative analysis of two prominent deep learning architectures, the Convolutional Neural ...
Augustiar Mahendra Mochammad, Firman Nurdiyansyah, Fitri Marisa   +2 more
doaj   +1 more source