Image processing and machine learning‐based bone fracture detection and classification using X‐ray images

Şahin, Muhammet

doi:10.1002/ima.22849

Cited by 24 publications

(9 citation statements)

References 30 publications

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“…Therefore, the cross-validation method is employed, which provides a performance result for all parts of the model’s dataset. To use cross-validation, the data are separated into equal sections based on a certain ratio 35 , 36 . In this study, a fivefold cross-validation technique is employed, which means that the data are divided into five equal parts.…”

Section: Methodsmentioning

confidence: 99%

“…To use cross-validation, the data are separated into equal sections based on a certain ratio. 35,36 In this study, a fivefold cross-validation technique is employed, which means that the data are divided into five equal parts. This ensures that the model's performance is evaluated on all parts of the dataset, increasing the reliability of the model's overall performance.…”

Section: Cross-validationmentioning

confidence: 99%

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Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Ulutas,

Emin Sahin

2023

J. Electron. Imag.

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.Plant diseases are a major concern for farmers worldwide as they can cause significant economic losses. Among vegetable crops, cucumber is particularly susceptible to various diseases, including fusarium and mildew. We present a unique dataset of cucumber leaf images collected from the Carsamba and Silifke districts of Turkey. This dataset is used in both newly proposed convolutional neural networks and pre-trained models available in the literature in addition to new ensemble models for disease prediction and classification. To optimize the classification accuracy, the grid search method is employed to identify the best combinations of base models and ensemble learning methods. The classification process is carried out using newly designed triple ensemble models and utilized fivefold cross-validation, and the results are evaluated using accuracy, F1-score, and receiver operating characteristic curves. The highest accuracy rate achieved in our study is 93.79%, which demonstrates the effectiveness of our approach for accurately identifying cucumber leaf diseases. Our proposed approach is not only accurate but also effective, making it a practical solution for farmers to use to quickly identify and control diseases in their crops. Our study provides valuable insights into the application of deep learning techniques for the classification of cucumber plant diseases and can serve as a useful model for further research in this field.

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Section: Methodsmentioning

confidence: 99%

Section: Cross-validationmentioning

confidence: 99%

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Ulutas,

Emin Sahin

2023

J. Electron. Imag.

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“…Several pre-processing methods are involved in this stage: noise removal, distortion removal, colour space conversion, image resizing and cropping, smoothing, enhancement, etc. [21]. The primary stage of pre-processing is resizing the input images.…”

Section: B Pre-processingmentioning

confidence: 99%

A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

Ulutaş

Aslantaş

2023

ELEKTRON ELEKTROTECH

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Agriculture is crucial to economic growth and development, and maintaining high-quality, disease-free plants is crucial to its success. Early detection of plant diseases, which can be caused by environmental factors, fungi, bacteria, and viruses, is essential to implement appropriate treatments. Tomatoes, which are one of the most vital food crops, are susceptible to diseases that can result in significant economic losses in agriculture. This study introduces a method to evaluate the health of tomato leaf using image processing techniques and machine learning algorithms. A dataset of 1,778 images of healthy and infected tomato leaves was collected from tomato planting areas in the Turkish provinces of Samsun and Mersin. Sixteen advanced machine learning algorithms were used for classification, and the optimal hyperparameters for each algorithm were determined using a grid search approach. The classifiers were executed on Jetson Nano and TX2 embedded systems. The experimental results indicate that the Random Forest classifier outperformed other algorithms, achieving approximately 99 % accuracy in detecting and classifying the health status of tomato leaves. The proposed system enables faster and more accurate detection, allowing farmers to classify plants as infected or healthy, ultimately improving decision-making on treatment and pest management strategies.

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“…The use of machine learning in orthopaedics is becoming more and more widespread, as demonstrated by the research of Lalehzarian et al (2021) [ 13 ]. In particular, the application of machine learning to the diagnosis and treatment of ankle fractures is gaining traction [ 14 , 15 ]. By utilizing patient data and imaging scans, machine learning algorithms can accurately predict the probability of an ankle fracture and its severity.…”

Section: Introductionmentioning

confidence: 99%

Harnessing ResNet50 and SENet for enhanced ankle fracture identification

Wang,

Ying,

Liu

et al. 2024

BMC Musculoskelet Disord

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Background Ankle fractures are prevalent injuries that necessitate precise diagnostic tools. Traditional diagnostic methods have limitations that can be addressed using machine learning techniques, with the potential to improve accuracy and expedite diagnoses. Methods We trained various deep learning architectures, notably the Adapted ResNet50 with SENet capabilities, to identify ankle fractures using a curated dataset of radiographic images. Model performance was evaluated using common metrics like accuracy, precision, and recall. Additionally, Grad-CAM visualizations were employed to interpret model decisions. Results The Adapted ResNet50 with SENet capabilities consistently outperformed other models, achieving an accuracy of 93%, AUC of 95%, and recall of 92%. Grad-CAM visualizations provided insights into areas of the radiographs that the model deemed significant in its decisions. Conclusions The Adapted ResNet50 model enhanced with SENet capabilities demonstrated superior performance in detecting ankle fractures, offering a promising tool to complement traditional diagnostic methods. However, continuous refinement and expert validation are essential to ensure optimal application in clinical settings.

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Image processing and machine learning‐based bone fracture detection and classification using X‐ray images

Cited by 24 publications

References 30 publications

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

Harnessing ResNet50 and SENet for enhanced ankle fracture identification

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