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

---

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

Horticulturae, Journal Year: 2023, Volume and Issue: 9(2), P. 149 - 149

Published: Jan. 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.

Language: Английский

---

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 178, P. 108798 - 108798

Published: June 25, 2024

Language: Английский

---

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

Diagnostics, Journal Year: 2022, Volume and Issue: 12(12), P. 2926 - 2926

Published: Nov. 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.

Language: Английский

---

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

---

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

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(3), P. 1916 - 1916

Published: Feb. 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.

Language: Английский

---

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

---

CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection

Renewable Energy, Journal Year: 2022, Volume and Issue: 203, P. 870 - 880

Published: Dec. 22, 2022

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

Published: Jan. 2, 2023

Language: Английский

---

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

---