Dual spin max pooling convolutional neural network for solar cell crack detection
This paper presents a solar cell crack detection system for use in photovoltaic (PV) assembly
units. The system utilizes four different Convolutional Neural Network (CNN) architectures …
units. The system utilizes four different Convolutional Neural Network (CNN) architectures …
Automated hyperparameter tuning for crack image classification with deep learning
Deep learning methods have relevant applications in crack detection in buildings. However,
one of the challenges in this field is the hyperparameter tuning process for convolutional …
one of the challenges in this field is the hyperparameter tuning process for convolutional …
Automatic classification of colour fundus images for prediction eye disease types based on hybrid features
A Shamsan, EM Senan, HSA Shatnawi - Diagnostics, 2023 - mdpi.com
Early detection of eye diseases is the only solution to receive timely treatment and prevent
blindness. Colour fundus photography (CFP) is an effective fundus examination technique …
blindness. Colour fundus photography (CFP) is an effective fundus examination technique …
Advanced Hybridization and Optimization of DNNs for Medical Imaging: A Survey on Disease Detection Techniques
MK Bohmrah, H Kaur - Artificial Intelligence Review, 2025 - Springer
Due to the high classification accuracy and fast computational speed offered by Deep
Neural Networks (DNNs), they have been widely used for the design and development of …
Neural Networks (DNNs), they have been widely used for the design and development of …
Clinical decision support framework for segmentation and classification of brain tumor MRIs using a U-Net and DCNN cascaded learning algorithm
Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of
computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic …
computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic …
Improving the robustness and quality of biomedical cnn models through adaptive hyperparameter tuning
Deep learning is an obvious method for the detection of disease, analyzing medical images
and many researchers have looked into it. However, the performance of deep learning …
and many researchers have looked into it. However, the performance of deep learning …
RNN and biLSTM fusion for accurate automatic epileptic seizure diagnosis using EEG signals
Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to
seizures alone. They comprise a wide spectrum of problems that might impair and reduce …
seizures alone. They comprise a wide spectrum of problems that might impair and reduce …
[HTML][HTML] A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks
Abstract Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL)
research for their architectural advantages. CNN relies heavily on hyperparameter …
research for their architectural advantages. CNN relies heavily on hyperparameter …
Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection
Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their
performance and reliability. The development of convolutional neural networks (CNNs) has …
performance and reliability. The development of convolutional neural networks (CNNs) has …
Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data
Predicting sea wave parameters such as significant wave height (SWH) has recently been
identified as a critical requirement for maritime security and economy. Earth observation …
identified as a critical requirement for maritime security and economy. Earth observation …