Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Ekmekyapar, Tuba; Taşcı, Burak

doi:10.3390/diagnostics13193030

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Resnet18 [38] 74.83 Resnet50 [38] 79.15 Resnet101 [38] 79.12 Darknet19 [39] 76.99 Mobilenetv2 [40,41] 77.47 Darknet53 [39] 76.20 Xception [42] 77.52 Efficientnetb0 [29] 81.18 Shufflenet [43] 78.10 Nasnetmobile [44] 75.70 Nasnetlarge [44] 77.09 Densenet201 [45] 77.86 Inceptionv3 [46] 76.63 Inceptionresnetv2 [47] 79.42…”

Section: Pre-trained Cnn Accuracy (%)mentioning

confidence: 99%

Fault Detection in Solar Energy Systems: A Deep Learning Approach

Duranay

2023

Electronics

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While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely impact energy production, necessitating the rapid and effective detection of such faults. This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems. A dataset comprising 20,000 images, derived from infrared solar modules, was utilized in this study, consisting of 12 classes: cell, cell-multi, cracking, diode, diode-multi, hot spot, hot spot-multi, no-anomaly, offline-module, shadowing, soiling, and vegetation. The methodology employed the exemplar Efficientb0 model. From the exemplar model, 17,000 features were selected using the NCA feature selector. Subsequently, classification was performed using an SVM classifier. The proposed method applied to a dataset consisting of 12 classes has yielded successful results in terms of accuracy, F1-score, precision, and sensitivity metrics. These results indicate average values of 93.93% accuracy, 89.82% F1-score, 91.50% precision, and 88.28% sensitivity, respectively. The proposed method in this study accurately classifies photovoltaic panel defects based on images of infrared solar modules.

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Section: Pre-trained Cnn Accuracy (%)mentioning

confidence: 99%

Fault Detection in Solar Energy Systems: A Deep Learning Approach

Duranay

2023

Electronics

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“…Some of these studies are provided below. In this manner, it becomes possible to alleviate the workload of healthcare professionals, utilize time more efficiently, and enhance diagnostic accuracy [13][14][15]. The development of computer-aided diagnostic systems holds particular significance for less developed and developing countries where there is an inadequacy of specialized experts.…”

Section: Introductionmentioning

confidence: 99%

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

Kaya,

Taşcı

2023

Diagnostics

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The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.

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Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Cited by 2 publications

References 32 publications

Fault Detection in Solar Energy Systems: A Deep Learning Approach

Fault Detection in Solar Energy Systems: A Deep Learning Approach

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

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