Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System DOI
Anna Anbumozhi,

A Shanthini

Network Computation in Neural Systems, Год журнала: 2024, Номер 35(4), С. 463 - 487

Опубликована: Июль 2, 2024

Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss groundnut plant growth, which will directly diminish quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Leaf Disease Identification System (GLDI-WDCGAN-AOA) proposed in this paper. The pre-processed output fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using (WDCGAN), input images classified into Healthy leaf, early spot, late nutrition deficiency, rust. Finally, weight parameters WDCGAN optimized Aquila Optimization Algorithm (AOA) achieve high accuracy. GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, 18.65% higher accuracy 24.78%, 23.24%, 28.98% lower error rate analysed with existing methods, such as Real-time automated identification categorization disease utilizing hybrid machine learning methods (GLDI-DNN), Online peanut data balancing method along deep transfer (GLDI-LWCNN), learning-driven depending on progressive scaling precise infections (GLDI-CNN), respectively.

Язык: Английский

Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning DOI Creative Commons
Qanita Bani Baker, Mahmoud Hammad, Mohammad AL-Smadi

и другие.

Journal of Imaging, Год журнала: 2024, Номер 10(10), С. 250 - 250

Опубликована: Окт. 13, 2024

The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. early diagnosis COVID-19 patients emerged as a pivotal strategy in mitigating the disease. Automated using Chest X-ray (CXR) imaging significant potential for facilitating large-scale screening epidemic control efforts. This paper introduces novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) accurate detection. employed datasets each comprised 15,000 images. We addressed both binary (Normal vs. Abnormal) multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, InceptionResNet-V2) As result, Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, 97.89% F1-score classification, while multi-classification it yielded 87.73% 90.20% an 87.49% F1-score. Moreover, other utilized models, such competitive performance compared with many recent works.

Язык: Английский

Процитировано

3
Detection of COVID-19: A Metaheuristic-Optimized Maximally Stable Extremal Regions Approach DOI Open Access

Víctor García-Gutiérrez,

Adrián González, Erik Cuevas

и другие.

Symmetry, Год журнала: 2024, Номер 16(7), С. 870 - 870

Опубликована: Июль 9, 2024

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration alternative approaches, including analysis lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in images indicative by combining maximally stable extremal regions (MSER) method metaheuristic algorithms. MSER is efficient and effective under various adverse conditions, utilizing symmetry as key property detect despite changes scaling or lighting. However, calibrating challenging. Our approach transforms this calibration into an optimization task, employing algorithms such Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), Genetic Algorithms (GA) find optimal parameters for MSER. By automating process through optimization, we overcome primary disadvantage method. innovative combination enables precise abnormal characteristic without need extensive datasets labeled training images, unlike deep learning methods. methodology was rigorously tested across multiple databases, quality evaluated using indices. experimental results demonstrate robust capability our support healthcare professionals accurately detecting COVID-19, highlighting its significant potential effectiveness practical medical diagnostics image analysis.

Язык: Английский

Процитировано

1
Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images DOI
M. Maheswari, Mohamed Uvaze Ahamed Ayoobkhan,

C P Shirley

и другие.

Medical & Biological Engineering & Computing, Год журнала: 2024, Номер 62(11), С. 3311 - 3325

Опубликована: Июнь 4, 2024

Язык: Английский

Процитировано

0
Authentication of multiple transaction using enhanced Elman spike neural network optimized with glowworm swarm optimization DOI

S. Mary Joans,

J. S. Leena Jasmine,

P. Ponsudha

и другие.

Wireless Networks, Год журнала: 2024, Номер unknown

Опубликована: Июнь 23, 2024

Язык: Английский

Процитировано

0
Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System DOI
Anna Anbumozhi,

A Shanthini

Network Computation in Neural Systems, Год журнала: 2024, Номер 35(4), С. 463 - 487

Опубликована: Июль 2, 2024

Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss groundnut plant growth, which will directly diminish quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Leaf Disease Identification System (GLDI-WDCGAN-AOA) proposed in this paper. The pre-processed output fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using (WDCGAN), input images classified into Healthy leaf, early spot, late nutrition deficiency, rust. Finally, weight parameters WDCGAN optimized Aquila Optimization Algorithm (AOA) achieve high accuracy. GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, 18.65% higher accuracy 24.78%, 23.24%, 28.98% lower error rate analysed with existing methods, such as Real-time automated identification categorization disease utilizing hybrid machine learning methods (GLDI-DNN), Online peanut data balancing method along deep transfer (GLDI-LWCNN), learning-driven depending on progressive scaling precise infections (GLDI-CNN), respectively.

Язык: Английский

Процитировано

0