Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

Healthcare, Год журнала: 2023, Номер 11(11), С. 1561 - 1561

Опубликована: Май 26, 2023

Pneumonia has been directly responsible for a huge number of deaths all across the globe. shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in way chest X-ray images are acquired and processed, impact quality consistency images. This challenging develop robust algorithms that accurately identify pneumonia types Hence, need robust, data-driven trained on large, high-quality datasets validated using range imaging techniques expert radiological analysis. In this research, deep-learning-based model demonstrated differentiating normal severe cases pneumonia. complete proposed system total eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, MobileNet. These models were simulated two having 5856 112,120 X-rays. The best accuracy obtained MobileNet values 94.23% 93.75% different datasets. Key hyperparameters including batch sizes, epochs, optimizers have considered during comparative interpretation these determine most appropriate model.

Язык: Английский

---

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(2), С. 5893 - 5927

Опубликована: Май 29, 2023

Abstract Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps timely detection of any infectious disease (IDs) essential to management diseases prediction future occurrences. Many scientists scholars have implemented DL techniques for pandemics, IDs other healthcare-related purposes, these outcomes are with various limitations research gaps. For purpose achieving an accurate, efficient less complicated DL-based system therefore, this study carried out systematic literature review (SLR) on pandemics using techniques. The survey anchored by four objectives state-of-the-art forty-five papers seven hundred ninety retrieved from different scholarly databases was analyze evaluate trend application areas pandemics. This used tables graphs extracted related articles online repositories analysis showed that good tool pandemic prediction. Scopus Web Science given attention current because they contain suitable scientific findings subject area. Finally, presents forty-four (44) studies technique performances. challenges identified include low performance model due computational complexities, improper labeling absence high-quality dataset among others. suggests possible solutions such as development improved or reduction output layer architecture pandemic-prone considerations.

Язык: Английский

---

Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection

Horticulturae, Год журнала: 2023, Номер 9(2), С. 149 - 149

Опубликована: Янв. 22, 2023

Tomatoes are one of the world’s greatest valuable vegetables and regarded as economic pillar numerous countries. Nevertheless, these harvests remain susceptible to a variety illnesses which can reduce destroy generation healthy crops, making early precise identification diseases critical. Therefore, in recent years, studies have utilized deep learning (DL) models for automatic tomato leaf illness identification. However, many methods based on single DL architecture that needs high computational ability update hyperparameters leading rise classification complexity. In addition, they extracted large dimensions from networks added complication. this study proposes pipeline utilizing three compact convolutional neural (CNNs). It employs transfer retrieve features out final fully connected layer CNNs more condensed high-level representation. Next, it merges benefit every CNN structure. Subsequently, applies hybrid feature selection approach select generate comprehensive set lower dimensions. Six classifiers procedure. The results indicate K-nearest neighbor support vector machine attained highest accuracy 99.92% 99.90% using 22 24 only. experimental proposed also compared with previous research verified its competing capacity.

Язык: Английский

---

CerCan·Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

Expert Systems with Applications, Год журнала: 2023, Номер 229, С. 120624 - 120624

Опубликована: Июнь 2, 2023

Язык: Английский

---

Deep hybrid model for Mpox disease diagnosis from skin lesion images

International Journal of Imaging Systems and Technology, Год журнала: 2024, Номер 34(2)

Опубликована: Фев. 26, 2024

Abstract This research presents DNLR‐NET, a novel model designed for automated and accurate diagnosis of MPox disease. The model's performance is constructed validated using carefully collected dataset from online repositories. DNLR‐NET begins by extracting deep features the DenseNet201 pre‐trained model, which exhibited superior compared to other models during comparison. obtained each dense layer are then used train six classifiers, among logistic regression showcases best with extracted deep, feature. A comparative study earlier advanced CNN classifying same demonstrates that achieves an impressive accuracy 97.55%, outperforming base only attains 95.91% accuracy. emphasizes efficacy combining regression. Grid Search algorithm employed optimal hyperparameter extraction, creating multiple unified feature sets achieving highest classification fusion yields results ensemble techniques such as random forest support vector machines also reduces training time complexity. surpasses existing models, ML demonstrating its effectiveness potential clinical implementation in diagnosing MPox. promising outcomes advantage learning algorithms, particularly transfer learning, highlight significance adopting methodologies CNN‐based settings. Researchers clinicians strongly encouraged explore implement these improve efficiency diagnosis.

Язык: Английский

---

Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection

Technologies, Год журнала: 2025, Номер 13(2), С. 54 - 54

Опубликована: Фев. 1, 2025

The automated and precise classification of lung colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, ineffectiveness utilising multiscale features. To this end, the present research introduces CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction selection overcome aforementioned constraints. Initially, it extracts attributes two separate layers (pooling fully connected) three pre-trained CNNs (MobileNet, ResNet-18, EfficientNetB0). Second, uses benefits canonical correlation analysis for dimensionality reduction pooling layer reduce complexity. In addition, features encapsulate both high- low-level representations. Finally, benefit multiple network architectures while reducing proposed merges dual variables then applies variance (ANOVA) Chi-Squared most discriminative integrated CNN architectures. is assessed LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, k-nearest neighbours. experimental results exhibited outstanding performance, attaining 99.8% accuracy cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding performance individual markedly diminishing framework’s capacity sustain exceptional limited set renders especially advantageous clinical applications where diagnostic precision efficiency critical. These findings confirm efficacy multi-CNN, multi-layer methodology enhancing mitigating constraints systems.

Язык: Английский

---

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

Diagnostics, Год журнала: 2022, Номер 12(12), С. 2926 - 2926

Опубликована: Ноя. 23, 2022

Among the leading causes of mortality and morbidity in people are lung colon cancers. They may develop concurrently organs negatively impact human life. If cancer is not diagnosed its early stages, there a great likelihood that it will spread to two organs. The histopathological detection such malignancies one most crucial components effective treatment. Although process lengthy complex, deep learning (DL) techniques have made feasible complete more quickly accurately, enabling researchers study lot patients short time period for less cost. Earlier studies relied on DL models require computational ability resources. Most them depended individual extract features high dimension or perform diagnoses. However, this study, framework based multiple lightweight proposed utilizes several transformation methods feature reduction provide better representation data. In context, histopathology scans fed into ShuffleNet, MobileNet, SqueezeNet models. number acquired from these subsequently reduced using principal component analysis (PCA) fast Walsh-Hadamard transform (FHWT) techniques. Following that, discrete wavelet (DWT) used fuse FWHT's obtained three Additionally, models' PCA concatenated. Finally, diminished as result FHWT-DWT fusion processes four distinct machine algorithms, reaching highest accuracy 99.6%. results show can distinguish variants with lower complexity compared existing methods. also prove utilizing reduce offer superior interpretation data, thus improving diagnosis procedure.

Язык: Английский

---

Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Applied Sciences, Год журнала: 2023, Номер 13(3), С. 1916 - 1916

Опубликована: Фев. 2, 2023

Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for timely identification of cervical but it susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored identify cancer order enhance conventional testing procedure. In attain remarkable classification results, current CAD systems require pre-segmentation steps extraction cells from pap slide, which complicated task. Furthermore, some models use only hand-crafted feature cannot guarantee sufficiency phases. addition, if there are few data samples, such as cell datasets, deep learning (DL) alone not perfect choice. existing obtain attributes one domain, integration features multiple domains usually increases performance. Hence, this article presents model based on extracting domain. It does process thus less complex than methods. employs three compact DL high-level spatial rather utilizing an individual with large number parameters and layers used CADs. Moreover, retrieves several statistical textural descriptors including time–frequency instead employing single domain demonstrate clearer representation features, case examines influence each set handcrafted accuracy independently hybrid. then consequences combining obtained CNN combined features. Finally, uses principal component analysis merge entire investigate effect merging numerous various results. With 35 components, achieved by quatric SVM proposed reached 100%. performance described proves that able boost accuracy. Additionally, comparative analysis, along other present studies, shows competing capacity CAD.

Язык: Английский

---

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Chemometrics and Intelligent Laboratory Systems, Год журнала: 2023, Номер 233, С. 104750 - 104750

Опубликована: Янв. 2, 2023

Язык: Английский

---

GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks

Diagnostics, Год журнала: 2023, Номер 13(2), С. 171 - 171

Опубликована: Янв. 4, 2023

One of the most serious and dangerous ocular problems in premature infants is retinopathy prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make safe, accurate, low-cost diagnosis ROP. All previous CAD for ROP original fundus images. Unfortunately, learning discriminative representation from ROP-related images difficult. Textural analysis techniques, such as Gabor wavelets (GW), demonstrate significant texture information that artificial intelligence (AI) based models improve accuracy. In this paper, an effective automated tool, namely GabROP, on GW multiple deep (DL) proposed. Initially, GabROP analyzes using generates several sets Next, these are used train three convolutional neural networks (CNNs) independently. Additionally, actual pictures build networks. Using discrete wavelet transform (DWT), features retrieved every CNN trained with various combined create textural-spectral-temporal demonstration. Afterward, each CNN, concatenated spatial obtained Finally, all incorporated cosine (DCT) lessen size caused by fusion process. The outcomes show it accurate efficient ophthalmologists. effectiveness compared recently developed techniques. Due GabROP's superior performance competing tools, ophthalmologists may be able identify more reliably precisely, which could result reduction effort examination time.

Язык: Английский

---