Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images

Computers in Biology and Medicine, Год журнала: 2024, Номер 169, С. 107914 - 107914

Опубликована: Янв. 4, 2024

Язык: Английский

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Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 95, С. 106330 - 106330

Опубликована: Апрель 25, 2024

Язык: Английский

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Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds

Plant Methods, Год журнала: 2025, Номер 21(1)

Опубликована: Янв. 9, 2025

Язык: Английский

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Portable system to detect starch adulteration in turmeric using NIR spectroscopy

Food Control, Год журнала: 2023, Номер 155, С. 110095 - 110095

Опубликована: Сен. 11, 2023

Язык: Английский

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Modified transfer learning frameworks to identify potato leaf diseases

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(17), С. 50401 - 50423

Опубликована: Ноя. 6, 2023

Язык: Английский

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Deep learning for lungs cancer detection: a review

Artificial Intelligence Review, Год журнала: 2024, Номер 57(8)

Опубликована: Июль 8, 2024

Abstract Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating diagnosis classification diseases. In this study, we use variety medical imaging modalities, including X-rays, WSI, CT scans, MRI, thoroughly investigate techniques field classification. This study conducts comprehensive Systematic Literature Review (SLR) using for research, providing overview methodology, cutting-edge developments, quality assessments, customized approaches. It presents data from reputable journals concentrates years 2015–2024. solve difficulty manually identifying selecting abstract features images. includes wide range methods classifying but focuses most popular method, Network (CNN). CNN can achieve maximum accuracy because its multi-layer structure, automatic weights, capacity communicate local weights. Various algorithms shown with performance measures like precision, accuracy, specificity, sensitivity, AUC; consistently shows greatest accuracy. The findings highlight important contributions DCNN improving detection classification, making them an invaluable resource researchers looking gain greater knowledge learning’s function applications.

Язык: Английский

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Small size CNN (CAS-CNN), and modified MobileNetV2 (CAS-MODMOBNET) to identify cashew nut and fruit diseases

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Апрель 2, 2024

Язык: Английский

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Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Май 27, 2024

Abstract Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, prone to ambiguous interpretations. This study proposes an advanced machine learning model designed enhance lung stage classification using CT scan images, aiming overcome these limitations by offering faster, non-invasive, reliable tool. Utilizing the IQ-OTHNCCD dataset, comprising scans from various stages healthy individuals, we performed extensive preprocessing including resizing, normalization, Gaussian blurring. A Convolutional Neural Network (CNN) was then trained this preprocessed data, class imbalance addressed Synthetic Minority Over-sampling Technique (SMOTE). The model’s performance evaluated through metrics such as precision, recall, F1-score, ROC curve analysis. results demonstrated accuracy 99.64%, F1-score values exceeding 98% across all categories. SMOTE enhanced ability classify underrepresented classes, contributing robustness These findings underscore potential in transforming diagnostics, providing high classification, which could facilitate detection tailored treatment strategies, ultimately improving patient outcomes.

Язык: Английский

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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(6), С. 3267 - 3301

Опубликована: Фев. 19, 2024

Язык: Английский

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CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(11), С. 31733 - 31758

Опубликована: Сен. 19, 2023

Язык: Английский

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