DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs

Electronics, Journal Year: 2023, Volume and Issue: 12(8), P. 1860 - 1860

Published: April 14, 2023

Infectious disease-related illness has always posed a concern on global scale. Each year, pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, lung opacity (LO) cause millions of deaths because they all affect the lungs. Early detection diagnosis can help create chances for better care in circumstances. Numerous tests, including molecular tests (RT-PCR), complete blood count (CBC) Monteux tuberculin skin (TST), ultrasounds, are used to detect classify these diseases. However, take lot time, have 20% mistake rate, 80% sensitive. So, with aid doctor, radiographic such as computed tomography (CT) chest radiograph images (CRIs) disorders. With CRIs or CT-scan images, there is danger that features various diseases’ diagnoses will overlap. The automation method necessary correctly diseases using CRIs. key motivation behind study was no identifying classifying (LO, pneumonia, VP, BP, TB, COVID-19) In this paper, DeepLungNet deep learning (DL) model proposed, which comprises 20 learnable layers, i.e., 18 convolution (ConV) layers 2 fully connected (FC) layers. architecture uses Leaky ReLU (LReLU) activation function, fire module, maximum pooling layer, shortcut connections, batch normalization (BN) operation, group making it novel classification framework. This useful DL-based disorders, we tested effectiveness suggested framework two datasets variety from different datasets. We performed experiments: five-class (TB, LO, normal) six-class (VP, normal, LO). framework’s average accuracy into normal an impressive 97.47%. verified performance our publicly accessible database agriculture sector order further assess its validate generalizability. offers efficient automated aids early disease. strategy significantly improves patient survival, possible treatments, limits transmission infectious illnesses throughout society.

Language: Английский

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TumorDetNet: A unified deep learning model for brain tumor detection and classification

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(9), P. e0291200 - e0291200

Published: Sept. 27, 2023

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for treatment process and helps to save lives a large number people worldwide. Because they are non-invasive spare patients from having unpleasant biopsy, magnetic resonance imaging (MRI) scans frequently employed identify tumors. The manual identification tumors difficult requires considerable time due three-dimensional images that MRI scan one patient’s produces various angles. Moreover, variations in location, size, shape also make it challenging detect classify different types Thus, computer-aided diagnostics (CAD) systems have been proposed detection In this paper, we novel unified end-to-end deep learning model named TumorDetNet classification. Our framework employs 48 convolution layers with leaky ReLU (LReLU) activation functions compute most distinctive feature maps. average pooling dropout layer used learn patterns reduce overfitting. Finally, fully connected softmax into multiple types. We assessed performance our method on six standard Kaggle datasets classification (malignant benign), (glioma, pituitary, meningioma). successfully identified remarkable accuracy 99.83%, classified benign malignant ideal 100%, meningiomas, gliomas 99.27%. These outcomes demonstrate potency suggested methodology reliable categorization

Language: Английский

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CIFF-Net: Contextual image feature fusion for Melanoma diagnosis

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 88, P. 105673 - 105673

Published: Nov. 6, 2023

Language: Английский

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LRCTNet: A lightweight rectal cancer T-staging network based on knowledge distillation via a pretrained swin transformer

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107696 - 107696

Published: Feb. 12, 2025

Language: Английский

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Automated Detection of COVID-19 Using Deep Convolutional Neural Network (CNNs) Using Chest Radiograph and CT Scan Images

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 61 - 74

Published: Jan. 1, 2025

Language: Английский

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Blockchain reputation-based consensus mechanism for distributed medical supply chain drug traceability with pyramidal ShuffleNet

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: March 31, 2025

Language: Английский

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IncV3-BLSTM: a multi-label inceptionV3-BLSTM model for predicting potential side effects of COVID-19 drugs

Annals of Mathematics and Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

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Deep Learning‐Based Noninvasive Blood Glucose Estimation

Journal of Engineering, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Estimating blood glucose levels (BGLs) noninvasively is a rapidly advancing field driven by the need for effective and painless monitoring solutions diabetic patients. This study explores deep learning (DL) models applied to noninvasive techniques accurate BGL estimation. Thermal images were collected Type I diabetes after confirming BGLs using glucometer. DL then employed classify thermal into three classes (low, high, normal). DarkNet ShuffleNet convolutional neural networks (CNNs) are used image get best performance, with an overall accuracy of 98% 100% CNN.

Language: Английский

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Improved Breast Cancer Classification Approach Using Hybrid Deep Learning Strategies for Tumor Segmentation

Sensing and Imaging, Journal Year: 2024, Volume and Issue: 25(1)

Published: June 1, 2024

Language: Английский

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Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

Diagnostics, Journal Year: 2023, Volume and Issue: 13(3), P. 492 - 492

Published: Jan. 29, 2023

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in carpal tunnel. The determination severity essential provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used classify diseases, make decisions, and create new It also medical research implement predictive models. However, despite growth based on ML Deep Learning (DL), CTS still relatively scarce. While few studies have developed models predict diagnosis CTS, no model has been presented comprehensive data. Therefore, this study classification for determining using algorithms. This included 80 patients with other diseases an overlap symptoms such as cervical radiculopathysasas, de quervian tendinopathy, peripheral neuropathy, who underwent ultrasonography (US)-guided hydrodissection. was classified into mild, moderate, severe grades. In our study, we aggregated data from neuropathy. dataset randomly split training test data, at 70% 30%, respectively. proposed achieved promising results 0.955%, 0.963%, 0.919% terms accuracy, precision, recall, addition, machine predicts probability patient improving after hydro-dissection injection process three different months (one, three, six). accuracy six 0.912%, 0.901%, one month 0.877%. overall performance predicting prognosis outperforms prediction months. We utilized statistics tests (significance test, Spearman’s correlation two-way ANOVA test) determine effect treatment. Our data-driven decision support tools help which operate order avoid associated risks expenses surgery.

Language: Английский

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