Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping ana...
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Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset's images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm do
作者：
Yoon, Kun SuKim, Wan-JinKorea Polytech
Dept Aviat Control Syst Aviat Campus46 Daehak Gil Sacheon Si 52549 Gyeongsangnam D South Korea Agcy Def Dev
Inst R&D 6 Jinhae POB 18 Changwon Si 51678 Gyeongsangnam D South Korea
In the field of computer-aided recognition, edge feature is one of the key factors to determine recognition performance. Comparing to an optical image, since sonar image via acoustic wave is easily influenced by under...
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In the field of computer-aided recognition, edge feature is one of the key factors to determine recognition performance. Comparing to an optical image, since sonar image via acoustic wave is easily influenced by underwater environments such as particle density, temperature, and current, edge information should be boosted. Some image preprocessing techniques based on transform domain such as wavelet and curvelet may be good candidates but conventional methods show not only the possibility of enhancing edge features but also the limitation due to the absence of consideration to the edge direction. This study proposes an improved edge enhancement method based on curvelet transform (CVT), which is able to find out edge direction. The proposed method (PM) calculates the maximum value by ridgelet coefficients on each angular line, derived from the sub-step of the CVT, and the real edge direction is determined by local maxima selection after finding the azimuth of this value. In addition, selective sharpening is performed according to the feature information of edge. Experimental results have shown that the PM is comparable with conventional methods in terms of edge intensity, recognition rate, and peak signal-to-noise ratio.
Potato late blight is one of the commonserious diseases, caused by Phytophthora infestans, with major risks for agriculture production and food supply. This study addresses this challenge by critically evaluating the ...
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ISBN:
(纸本)9783031686740;9783031686757
Potato late blight is one of the commonserious diseases, caused by Phytophthora infestans, with major risks for agriculture production and food supply. This study addresses this challenge by critically evaluating the effect of normalization, image-wise standardization, and dataset-wise standardization preprocessingtechniques on a YOLOv8m model designed for blight detection. The reported results of the normalization show it remains robust for generalization, especially in the case of unseen data with an mAP50 of 99.4%. At the same time, imagewise standardization still is an acceptable alternative with an mAP50 of 73.3%. Dataset-wise standardization is reported to showlesser efficacy in newdata scenarios resulting in 21.7% of mAP50. The YOLOv8m has a compact and streamlined architecture that projects preprocessing to be a core factor in disease detection, paving the way for further advances in precision agriculture.
Attention deficit hyperactivity disorder (ADHD) is a psychiatric condition that affects millions of children and many times last into adulthood. There is no single test that can show whether a person has ADHD. The sym...
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ISBN:
(纸本)9781538668528
Attention deficit hyperactivity disorder (ADHD) is a psychiatric condition that affects millions of children and many times last into adulthood. There is no single test that can show whether a person has ADHD. The symptoms vary from person to person. Therefore, it is hard to diagnose ADHD contrary to many physical illnesses. Our aim is to create methods to minimize human effort and increase accuracy of diagnosis of ADHD. We collected structural Magnetic Resonance images (MRI) from 26 subjects: 11 controls and 15 children diagnosed with ADHD. The data was provided from NPIstanbul NeuroPsychiatric Hospital. We developed automatic, effective, rapid, and accurate framework for diagnosing ADHD. The models were built on the k-nearest neighbors algorithm (KNN) and naive Bayes using Matlab machine learning toolbox. Shape and texture feature extraction technique was used. Area, Perimeter, Eccentricity, EquivDiameter, Major Axis Length, Minor Axis Length, Orientation are characteristics used for shape feature extraction technique. Textural features of a magnetic resonance imaging were represented with first (mean, variance, skewness, kurtosis) and second order statistical (contrast, correlation, homogeneity, energy) based feature extraction techniques. Gray and white regions were extracted using k-means algorithms. Local features were extracted from these regions by shape and texture methods. Global features were extracted with second order statistics which is called gray level co-occurrence method. The most important attribute was determined by using principal component analysis. The experiments were conducted on a full training dataset including 26 instance and 5 fold cross validation was adopted for randomly sampling training and test sets. ADHD is successfully classified with 100 % accuracy by using the proposed method. The outcome of our study will reduce the number medical errors by informing physicians in their efforts of diagnosing ADHD.
Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have sh...
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Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our study employed convolutional neural networks (CNNs) to classify the severity of DR using retinal images from the EyePACS dataset, which includes 35,155 images categorized into five classes. Building on previous research that often classified DR into two classes, such as no DR and varying levels of DR, we found that while these studies typically used models like Inception V3, VGGNet, and ResNet, they focused on simplifying the diagnostic process by reducing the number of classes. However, our approach utilized a smaller, more flexible CNN architecture, allowing for a more detailed classification into five stages of DR. We employed various image preprocessing techniques, including grayscale conversion, background removal, and data augmentation, with our findings indicating that background removal significantly enhanced model performance, achieving a validation accuracy of 90.60%. This underscores the importance of sophisticated data preprocessing in medical imaging, and our study contributes to the ongoing development of automated DR diagnosis, potentially easing the burden on healthcare systems and improving patient outcomes.
Agriculture plays a vital role in a country's economy. Thus, better yields of crops are required for growth of agriculture-based industries. Deep Learning (DL) techniques have led to remarkable achievements in ima...
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Agriculture plays a vital role in a country's economy. Thus, better yields of crops are required for growth of agriculture-based industries. Deep Learning (DL) techniques have led to remarkable achievements in image classification and recognition. However, DL networks rely heavily on large data sets to prevent overfitting. image augmentation is one of the DL techniques gaining attention in avoiding the risk of overfitting. The most common image augmentation techniques like rotation, zoom, and shift used in the existing research allow to generate new images from the original set and increases the images quantity but cannot minimize the misclassification error. The present research can provide a better solution to provide sufficient quantity of training images to a convolutional neural network model to handle the overfitting and classification problems. Therefore, two learning algorithms imagepreprocessing and transformation algorithm (IPTA) and image masking and REC-based hybrid segmentation algorithm (IMHSA) are proposed to address the problem of limited dataset and convolutional neural network model overfitting during classification. IPTA is an adaptive supervised learning approach to transform the original images into augmented ones and IMHSA is an unsupervised approach for Red, Green, Blue (RGB) image segmentation. Later, the Histogram threshold technique is applied to form all the possible regions used to split the diseased leaf into comparable regions. A novel convolutional neural network model is also proposed to evaluate the performance of the IPTA approach. The model is trained on two independent datasets, one generated before and one generated after IPTA was applied. Plots of precision and loss functions are used to assess the acquired results. The experimental results demonstrated that before using IPTA, the training accuracy was 83%, while the validation accuracy was 65%. After using IPTA, the proposed model attained a training accuracy of 74% and a vali
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