Performance evaluation of optimal ensemble learning approaches with PCA and LDA-based feature extraction for heart disease prediction

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 101, P. 107138 - 107138

Published: Nov. 16, 2024

Language: Английский

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Brain Tumor Detection by Image Segmentation Using Customized UNet Deep Learning Based Model

Published: May 2, 2024

Human brain is the most valuable organ that perform critical thinking and get best solution methodology for real life problem. So, proper care should be taken to keep this part safe from being damaged by tumor disease. When a misdiagnosed, patients may receive incorrect medical care, decreasing their chances of survival. Brain tumors are deadly condition that, in its worst case, can have very short expectancy. In order overcome these difficulties, suggested framework uses CNN large-scale trials detect utilizing deep learning model's segmentation process. It anticipated application regularization strategies like augmentation dropout will improve precision identification with efficient manner. paper, we present deep-learning method tumors. We made use publicly available Kaggle dataset included color MRI pictures both healthy brains were afflicted. The underwent preprocessing. A customized UNet model was employed. Here, customize adding 1 Convolution layer downsampling De-Convolution upsampling. With our model, achieved 99.80% train accuracy. For validation test phase, 99.78% & 99.75% accuracy, respectively.

Language: Английский

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Monkeypox Classification Using EfficientNetB3 Transfer Learning Model

Published: Aug. 28, 2024

Language: Английский

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Optimized Global Aware Siamese Network based Monkeypox disease classification using skin images

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 101, P. 107125 - 107125

Published: Nov. 18, 2024

Language: Английский

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Deep Learning Framework for Optimizing Early Detection of Measles Using Transfer Learning

Indus journal of bioscience research., Journal Year: 2024, Volume and Issue: 2(2), P. 985 - 998

Published: Dec. 15, 2024

Measles is a highly infectious viral disease that can have serious health consequences. Accurate and early diagnosis crucial. This study aims to enhance automated classification detection of this disease. To address the class imbalance, we augmented dataset normal images. Spatial features were extracted using convolutional neural networks, traditional classifiers, including support vector machine, Random Forest, logistic regression, k-nearest neighbors applied these features. Initial accuracy based solely on spatial was as follows: Forest 63%, SVM KNN 60%, Logistic Regression 63%. Through 10-fold cross-validation, mean accuracies recorded 65% for RF, 62% SVM, 60% KNN, 61% LR. Despite initial results, implementation transfer learning led significant improvements. By extracting probabilistic from RF models concatenating derived features, substantially enhanced. The improved model achieved 99% LR, with reaching 98%. Cross-validation confirmed robustness models, approximately 98% minimal standard deviations 0.01. findings demonstrate combining classifiers improves efficiency lesion approach shows potential clinical applications.

Language: Английский

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Hypergraph Convolutional Neural Networks for Clinical Diagnosis of Monkeypox Infections Using Skin Virological Images

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112673 - 112673

Published: Dec. 1, 2024

Language: Английский

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