A Deep Learning-based Fine-tuned Convolutional Neural Network Model for Plant Leaf Disease Detection DOI

Gurpreet Singh,

Kalpna Guleria, Shagun Sharma

и другие.

Опубликована: Окт. 6, 2023

The rapid proliferation of plant diseases poses a grave threat to global food security and agricultural productivity. To effectively address these challenges ensure sustainable practices, the timely accurate identification becomes paramount. Over recent years, deep learning techniques namely Convolutional Neural Networks (CNNs), have emerged as pivotal tool with potential revolutionize disease by performing effective feature extraction. This study focuses on development CNN model for automated identification. dataset implementation has been collected from Kaggel, which contains 32 varieties leaf including normal leaves. proposed 13 different convolutional, 4 max pooling, 1 flattening dense layer performance implemented in four scenarios applying complete 5, 10, 15, 20 epoch values. results depict that shown highest accuracy 98.70% at while 97.87%, 95.92%, 87.09% accuracies resulted values respectively. effectiveness this also compared existing work achieving accuracy.

Язык: Английский

A comprehensive review on federated learning based models for healthcare applications DOI
Shagun Sharma, Kalpna Guleria

Artificial Intelligence in Medicine, Год журнала: 2023, Номер 146, С. 102691 - 102691

Опубликована: Окт. 30, 2023

Язык: Английский

Процитировано

46
Transparency in Diagnosis: Unveiling the Power of Deep Learning and Explainable AI for Medical Image Interpretation DOI
Priya Garg, Meenakshi Sharma,

Parteek Kumar

и другие.

Arabian Journal for Science and Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Язык: Английский

Процитировано

3
A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble DOI Creative Commons
Qiuyu An, Wei Chen, Wei Shao

и другие.

Diagnostics, Год журнала: 2024, Номер 14(4), С. 390 - 390

Опубликована: Фев. 11, 2024

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in efficacy medical decision systems. This paper presents novel approach utilizing convolutional neural network that effectively amalgamates strengths EfficientNetB0 and DenseNet121, it is enhanced by suite attention mechanisms for refined classification. Leveraging pre-trained models, our employs multi-head, self-attention modules meticulous feature extraction from images. The model’s integration processing efficiency are further augmented channel-attention-based fusion strategy, one complemented residual block an attention-augmented enhancement dynamic pooling strategy. Our used dataset, which comprises comprehensive collection chest images, represents both healthy individuals those affected pneumonia, serves as foundation this research. study delves into algorithms, architectural details, operational intricacies proposed model. empirical outcomes model noteworthy, with exceptional performance marked accuracy 95.19%, precision 98.38%, recall 93.84%, F1 score 96.06%, specificity 97.43%, AUC 0.9564 on test dataset. These results not only affirm high diagnostic accuracy, but also highlight its promising potential real-world clinical deployment.

Язык: Английский

Процитировано

16
Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model DOI Creative Commons
Mudasir Ali, Mobeen Shahroz,

Urooj Akram

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 34691 - 34707

Опубликована: Янв. 1, 2024

Pneumonia is a potentially life-threatening infectious disease that typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds, or lung biopsies. Accurate diagnosis crucial wrong diagnosis, inadequate treatment lack of can cause serious consequences for patients may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts diagnosing pneumonia by assisting their decision-making process. By leveraging models, healthcare professionals enhance accuracy make informed decisions suspected having pneumonia. In this study, six models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50, Efficient-NetV2L are implemented evaluated. study also incorporates the Adam optimizer, which effectively adjusts epoch all models. trained on dataset 5856 X-ray images show 87.78%, 88.94%, 90.7%, 91.66%, 87.98%, 94.02% ResNet50 EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates highest proves its robustness detection. These findings highlight potential accurately detecting predicting based images, providing valuable support clinical improving patient treatment.

Язык: Английский

Процитировано

15
Medical Imaging-based Artificial Intelligence in Pneumonia: A Narrative Review DOI
Yanping Yang, Wenyu Xing, Yiwen Liu

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129731 - 129731

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

2
A deep learning-based model for biotic rice leaf disease detection DOI
Amandeep Kaur, Kalpna Guleria, Naresh Kumar Trivedi

и другие.

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(36), С. 83583 - 83609

Опубликована: Март 15, 2024

Язык: Английский

Процитировано

7
Comparative Analysis of ResNet-18 and ResNet-50 Architectures for Pneumonia Detection in Medical Imaging DOI
Ashutosh Gupta, Shreya Arora, Manoj Jain

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 355 - 365

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
AI-Driven Technology in Heart Failure Detection and Diagnosis: A Review of the Advancement in Personalized Healthcare DOI Open Access

Ikteder Akhand Udoy,

Omiya Hassan

Symmetry, Год журнала: 2025, Номер 17(3), С. 469 - 469

Опубликована: Март 20, 2025

Artificial intelligence (AI) is playing a dominant role in advancing heart failure detection and diagnosis, significantly furthering personalized healthcare. This review synthesizes AI-driven innovations by examining methodologies, applications, outcomes. We investigate the integration of machine learning algorithms, diverse datasets including electronic health records (EHRs), medical records, imaging data, clinical notes, deep models, neural networks to enhance diagnostic accuracy. Key advancements include prediction models that leverage real-time data from wearable devices alongside state-of-the-art AI systems trained on patient hospitals clinics. Notably, recent studies have reported accuracies ranging 86.7% as high 99.9%, with sensitivity specificity values often exceeding 97%, underscoring potential these improve early decision-making substantially. Our further explores impact symmetry asymmetry model design, highlighting symmetric architectures like U-Net offer computational efficiency structured feature extraction. In contrast, asymmetric rare conditions subtle patterns. Incorporating (DL) methods anomaly disease progression modeling reinforces their positive accuracy Furthermore, this identifies challenges current such quality, algorithmic transparency, bias, evaluation metrics, while outlining future research directions, integrating generative hybrid architectures, explainable techniques optimize practice.

Язык: Английский

Процитировано

1
Applications of Deep Learning Models on the Medical Images of Osteonecrosis of the Femoral Head (ONFH): A Comprehensive Review DOI Creative Commons
Jiao Wang, Yi Zheng,

Jun Luo

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 57613 - 57632

Опубликована: Янв. 1, 2024

Deep learning models have demonstrated promising results in the early and accurate diagnosis of osteonecrosis femoral head (ONFH), enabling detection informed surgical decision-making. The objective this review is to summarize applications deep on medical images ONFH. English papers were searched from CINAHL via EBSCOhost, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, Web Science. Sixteen studies (n = 16) eligible for data synthesis. Among these, five 5) focusing radiographs, ten 10) magnetic resonance imaging, one study 1) computed tomographic images. these included identifying ONFH normal or other hip pathologies, classifying severity, segmenting, detecting necrotic regions, predicting signs symptoms ONFH, potential after fracture fixation. Generally, good excellent classification performance discriminatory power; generally comparable that experienced physicians superior less physicians. However, external validity only moderate, as evidenced by testing set might be attributed relatively small size used during model training. we observed a shift CNN-based U-Net based (i.e., with encoder-decoder architecture). In addition streamlining segmentation, detection, procedures, future will explore multimodal attention, self-supervised learning, explainable models, augmentation through generative models.

Язык: Английский

Процитировано

4
DeepFungusDet: MobileNetV3 Model in Medical Imaging for Fungal Disease Detection DOI

Gurpreet Singh,

Kalpna Guleria, Shagun Sharma

и другие.

Опубликована: Март 1, 2024

A fungal infection in humans is a pathological state resulting from the infiltration and proliferation of fungi within body. Microorganisms known as are present air, water, soil, plants. The can cause skin to become red inflamed causing bad oral genital effects article presents deep learning technique for identifying infections using MobileNetV3, which compact resilient convolutional neural network (CNN). model trained on wide variety datasets, demonstrating its efficiency mobility real-time detection portable devices. categorize identify various across different conditions capabilities. findings result an excellent accuracy speed infections, indicating potential rapid accessible healthcare, agriculture, environmental monitoring. work investigates effectiveness MobileNetV3 named DeepFungusDet broad dataset containing infections. This has been implemented at numbers epochs highest identification 93.14% epoch 13 loss 0.4494, promise recognizing tool provides option via mobile devices, paving way future research use crucial field fungus identification. represent major step forward provide prospects developing practical diagnostic tools healthcare industry related fields.

Язык: Английский

Процитировано

3