HXAI-ML: A Hybrid Explainable Artificial Intelligence Based Machine Learning Model For Cardiovascular Heart Disease Detection

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104370 - 104370

Опубликована: Фев. 1, 2025

Язык: Английский

---

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

International Journal of Imaging Systems and Technology, Год журнала: 2023, Номер 34(1)

Опубликована: Дек. 22, 2023

Abstract Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis extensive 3D MRI images can be error‐prone and time‐consuming. This study introduces Deep Explainable Brain Tumor Network (DeepEBTDNet), a novel deep learning model binary classification MRIs as tumorous or normal. Employing sub‐image dualistic histogram equalization (DSIHE) enhanced image quality, DeepEBTDNet utilizes 12 convolutional layers with leaky ReLU (LReLU) activation feature extraction, followed by fully connected layer. Transparency interpretability are emphasized through application Local Interpretable Model‐Agnostic Explanations (LIME) method to explain predictions. Results demonstrate DeepEBTDNet's efficacy in tumor detection, even across datasets, achieving validation accuracy 98.96% testing 94.0%. underscores importance explainable AI healthcare, facilitating precise diagnoses transparent decision‐making early identification improved outcomes.

Язык: Английский

---

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

PLoS ONE, Год журнала: 2023, Номер 18(9), С. e0291200 - e0291200

Опубликована: Сен. 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

Язык: Английский

---

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

Frontiers in Plant Science, Год журнала: 2023, Номер 14

Опубликована: Окт. 11, 2023

Introduction Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early of diseases enables farmers to take preventative action, stopping the disease's transmission other sections. Plant are severe hazard food safety, but because essential infrastructure is missing in various places around globe, quick still difficult. The may experience variety attacks, from minor damage total devastation, depending on how infections are. Thus, early necessary optimize output prevent such destruction. physical examination produced low accuracy, required lot time, could not accurately anticipate disease. Creating an automated method capable classifying deal with these issues vital. Method This research proposes efficient, novel, lightweight DeepPlantNet deep learning (DL)-based architecture for predicting categorizing leaf diseases. proposed model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) three fully connected (FC) layers. framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, mix 3×3 1×1 filters, making it novel classification framework. Proposed can categorize images into many classifications. Results approach categorizes following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), maize common rust (MCR). achieved average accuracy 98.49 99.85in case eight-class three-class schemes, respectively. Discussion experimental findings demonstrated model's superiority alternatives. technique reduce financial losses by quickly effectively assisting professionals identifying

Язык: Английский

---

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2023, Номер 77(3), С. 3969 - 3992

Опубликована: Янв. 1, 2023

Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers efficient disease classification to overcome this. The Convn kernels used proposed (DTomatoDNet) is 1 × 1, which reduces number of parameters helps more detailed descriptive feature extraction classification. DTomatoDNet model trained from scratch determine success rate. 10,000 images (1000 per class) publicly accessible dataset, covering one healthy category nine categories, are utilized training approach. More specifically, we classified into Target Spot (TS), Early Blight (EB), Late (LB), Bacterial (BS), Leaf Mold (LM), Yellow Curl Virus (YLCV), Septoria (SLS), Spider Mites (SM), Mosaic (MV), Healthy (H). approach obtains accuracy 99.34%, demonstrating differentiating between could be mobile platforms because it designed with fewer layers. farmers can utilize methodology detect quickly easily once been integrated by developing application.

Язык: Английский

---

PediaPulmoDx: Harnessing Cutting Edge Preprocessing and Explainable AI for Pediatric Chest X-ray Classification with DenseNet121

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104320 - 104320

Опубликована: Фев. 1, 2025

Язык: Английский

---

Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review

Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e69068 - e69068

Опубликована: Март 7, 2025

Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of various TB diagnostic tools introduced is deemed sufficient on its own for pathway, so artificial intelligence (AI)-based methods have been developed address this issue. We aimed provide comprehensive evaluation AI-based algorithms detection across data modalities. Following PRISMA (Preferred Reporting Items Systematic Reviews and Meta-Analysis) 2020 guidelines, we conducted systematic review synthesize current knowledge topic. Our search 3 major databases (Scopus, PubMed, Association Computing Machinery [ACM] Digital Library) yielded 1146 records, which included 152 (13.3%) studies in our analysis. QUADAS-2 (Quality Assessment Diagnostic Accuracy Studies version 2) was performed risk-of-bias assessment all studies. Radiographic biomarkers (n=129, 84.9%) deep learning (DL; n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), DenseNet-121 (n=19, 12.5%) architectures being most common DL approach. The majority focused model development (n=143, 94.1%) used single modality approach (n=141, 92.8%). AI demonstrated good performance studies: mean accuracy=91.93% (SD 8.10%, 95% CI 90.52%-93.33%; median 93.59%, IQR 88.33%-98.32%), area under curve (AUC)=93.48% 7.51%, 91.90%-95.06%; 95.28%, 91%-99%), sensitivity=92.77% 7.48%, 91.38%-94.15%; 94.05% 89%-98.87%), specificity=92.39% 9.4%, 90.30%-94.49%; 95.38%, 89.42%-99.19%). different biomarker types showed accuracies 92.45% 7.83%), 89.03% 8.49%), 84.21% 0%); AUCs 94.47% 7.32%), 88.45% 8.33%), 88.61% 5.9%); sensitivities 93.8% 6.27%), 88.41% 10.24%), 93% specificities 94.2% 6.63%), 85.89% 14.66%), 0%) radiographic, molecular/biochemical, physiological types, respectively. reference standards 91.44% 7.3%), 93.16% 6.44%), 88.98% 9.77%); 90.95% 7.58%), 94.89% 5.18%), 92.61% 6.01%); 91.76% 7.02%), 93.73% 6.67%), 91.34% 7.71%); 86.56% 12.8%), 93.69% 8.45%), 92.7% 6.54%) bacteriological, human reader, combined standards, transfer (TL) increasing popularity (n=89, 58.6%). Notably, only 1 (0.7%) study domain-shift analysis detection. Findings from underscore considerable promise realm Future research endeavors should prioritize conducting analyses better simulate real-world scenarios PROSPERO CRD42023453611; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023453611.

Язык: Английский

---

Intensified Feature Engineering-Based Composite Model for Predicting Lung Diseases

Algorithms for intelligent systems, Год журнала: 2025, Номер unknown, С. 119 - 132

Опубликована: Янв. 1, 2025

Язык: Английский

---

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

Annals of Mathematics and Artificial Intelligence, Год журнала: 2025, Номер unknown

Опубликована: Март 31, 2025

Язык: Английский

---

Plant Disease Classification Using Deep Learning and the Hyperband Strategy

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 709 - 717

Опубликована: Янв. 1, 2025

Язык: Английский

---