ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 142, P. 105210 - 105210

Published: Jan. 5, 2022

Language: Английский

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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 5, 2025

"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as introduction. The proposed method, which combines CNNs, DWTs, SVMs, enhances accuracy of feature extraction classification. study employs DWT optimize enhance two integrated CNN models before classifying them with SVM following systematic procedure. PolynetDWTCADx most effective we evaluated. It capable attaining moderate level recall, well an area under curve (AUC) during testing. testing 92.3%, training 95.0%. This demonstrates distinguishing between noncancerous cancerous lesions in colon. We can also employ semantic segmentation algorithms U-Net architecture accurately segment regions. assessed model's exceptional success segmenting providing precise delineation malignant tissues using its maximal IoU value 0.93, based on intersection over union (IoU) scores. When these techniques are added PolynetDWTCADx, they give doctors detailed visual information needed for diagnosis planning treatment. These very good at finding separating has potential recognition management cancer, underscores.

Language: Английский

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An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques

Biosensors, Journal Year: 2022, Volume and Issue: 12(5), P. 299 - 299

Published: May 5, 2022

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, decrease the workload on healthcare structures. The present tools detect experience numerous shortcomings. Therefore, novel diagnostic are be examined enhance accuracy avoid limitations of these tools. Earlier studies indicated multiple structures cardiovascular alterations in cases which motivated realization using ECG data as a tool for diagnosing coronavirus. This study introduced automated based diagnose COVID-19. utilizes ten deep learning (DL) models various architectures. It obtains significant features from last fully connected layer each DL model then combines them. Afterward, presents hybrid feature selection chi-square test sequential search select features. Finally, it employs several machine classifiers perform two classification levels. A binary level differentiate between normal cases, multiclass discriminate other cardiac complications. proposed reached an 98.2% 91.6% levels, respectively. performance indicates that could used alternative means diagnosis

Language: Английский

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Gastrointestinal abnormality detection and classification using empirical wavelet transform and deep convolutional neural network from endoscopic images

Ain Shams Engineering Journal, Journal Year: 2022, Volume and Issue: 14(4), P. 101942 - 101942

Published: Aug. 24, 2022

With an intention to assist gastroenterologists, this work proposes intelligent method classify alimentary canal diseases such as Barrett's, Esophagitis, Hemorrhoids, Polyps, and Ulcerative colitis by using empirical wavelet transform (EWT) convolutional neural network (CNN). Here, a publicly available HyperKvasir dataset is used for the experimental work. The framework starts with several image pre-processing steps followed implementation of EWT. EWT helps decompose into modes extract specific patterns in images. These decomposed images are then fed proposed deep CNN disease classification two-levels. Finally, model evaluated based on performance metrics. result shows 96.65% accuracy, 0.9298 Matthews correlation coefficient (MCC) first level, 94.25% 0.8108 MCC second level classification. Lastly, justify efficacy method, comparative study carried out other contemporary techniques.

Language: Английский

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

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 229, P. 120624 - 120624

Published: June 2, 2023

Language: Английский

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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, Journal Year: 2023, Volume and Issue: 233, P. 104750 - 104750

Published: Jan. 2, 2023

Language: Английский

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

Diagnostics, Journal Year: 2023, Volume and Issue: 13(2), P. 171 - 171

Published: Jan. 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.

Language: Английский

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Multitask Deep Learning-Based Pipeline for Gas Leakage Detection via E-Nose and Thermal Imaging Multimodal Fusion

Chemosensors, Journal Year: 2023, Volume and Issue: 11(7), P. 364 - 364

Published: June 28, 2023

Innovative engineering solutions that are efficient, quick, and simple to use crucial given the rapid industrialization technology breakthroughs in Industry 5.0. One of areas receiving attention is rise gas leakage accidents at coal mines, chemical companies, home appliances. To prevent harm both environment human lives, automated detection identification type necessary. Most previous studies used a single mode data perform process. However, instead using source/mode, multimodal sensor fusion offers more accurate results. Furthermore, majority individual feature extraction approaches extract either spatial or temporal information. This paper proposes deep learning-based (DL) pipeline combine acquired via infrared (IR) thermal imaging an array seven metal oxide semiconductor (MOX) sensors forming electronic nose (E-nose). The proposed based on three convolutional neural networks (CNNs) models for bidirectional long-short memory (Bi-LSTM) detection. Two used, including intermediate multitask fusion. Discrete wavelet transform (DWT) utilized features extracted from each CNN, providing spectral–temporal representation. In contrast, fusion, discrete cosine (DCT) merge all obtained CNNs trained with data. results show approach has boosted performance reaching accuracy 98.47% 99.25% respectively. These indicate superior Therefore, system capable detecting accurately could be industrial applications.

Language: Английский

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Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 22, 2024

Abstract Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose faster more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated diagnosis of CRC. Nevertheless, most previous CAD obtained features from one CNN, these huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, system proposed called “Color-CADx” recognition. Different CNNs namely ResNet50, DenseNet201, AlexNet used end-to-end classification at different training–testing ratios. Moreover, extracted reduced discrete cosine transform (DCT). DCT also utilized acquire spectral representation. Afterward, further select set deep features. Furthermore, coefficients in step concatenated analysis variance (ANOVA) feature selection approach applied choose Finally, machine classifiers employed Two publicly available datasets were investigated which NCT-CRC-HE-100 K dataset Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% 96.8% ANOVA have successfully lowered dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy terms accuracy, as its performance surpasses recent advancements.

Language: Английский

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DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Diagnostics, Journal Year: 2021, Volume and Issue: 11(11), P. 2034 - 2034

Published: Nov. 3, 2021

Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis ROP. This paper proposes an automated reliable diagnostic tool based on DL techniques called DIAROP to support ophthalmologic It extracts significant features by first obtaining spatial from four Convolution Neural Networks (CNNs) using transfer learning then applying Fast Walsh Hadamard Transform (FWHT) integrate these features. Moreover, explores best-integrated extracted CNNs that influence its capability. The results indicate achieved accuracy 93.2% area under receiving operating characteristic curve (AUC) 0.98. Furthermore, performance is compared with recent ROP tools. Its promising shows may

Language: Английский

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