A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images DOI Creative Commons
Md. Nahiduzzaman, Md. Omaer Faruq Goni, Md. Shamim Anower

et al.

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 147512 - 147526

Published: Jan. 1, 2021

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable pneumonia. An experienced radiologist is required this. However, even an radiographer, it quite difficult time-consuming to diagnose due fuzziness CXR images. Also, identification can be erroneous involvement human judgment. Hence, authentic automated system play important here. cutting-edge technology, deep learning (DL) highly used every sector. There several existing methods but they have accuracy problems. study, automatic detection has been proposed by applying extreme machine (ELM) on Kaggle images (Pneumonia). Three models studied: classification using (ELM), ELM with hybrid convolutional neural network - principle component (CNN-PCA) based feature extraction (ECP), ECP which contrast-enhanced contrast limited adaptive histogram equalization (CLAHE). Among these three methods, final model provides optimistic result. It achieves recall score 98% 98.32% multiclass classification. On other hand, binary 100% 99.83% accuracy. The method also outperforms methods. outcome compared benchmarks that include accuracy, precision, recall, etc.

Language: Английский

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion DOI Creative Commons

Kiran Jabeen,

Muhammad Attique Khan, Majed Alhaisoni

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(3), P. 807 - 807

Published: Jan. 21, 2022

After lung cancer, breast cancer is the second leading cause of death in women. If detected early, mortality rates women can be reduced. Because manual diagnosis takes a long time, an automated system required for early detection. This paper proposes new framework classification from ultrasound images that employs deep learning and fusion best selected features. The proposed divided into five major steps: (i) data augmentation performed to increase size original dataset better Convolutional Neural Network (CNN) models; (ii) pre-trained DarkNet-53 model considered output layer modified based on augmented classes; (iii) trained using transfer features are extracted global average pooling layer; (iv) two improved optimization algorithms known as reformed differential evaluation (RDE) gray wolf (RGW); (v) fused probability-based serial approach classified machine algorithms. experiment was conducted Breast Ultrasound Images (BUSI) dataset, accuracy 99.1%. When compared with recent techniques, outperforms them.

Language: Английский

Citations

204
Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review DOI
Deepak Painuli, Suyash Bhardwaj, Utku Köse

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 146, P. 105580 - 105580

Published: May 5, 2022

Language: Английский

Citations

100
Analysis of DNA Sequence Classification Using CNN and Hybrid Models DOI Open Access
Hemalatha Gunasekaran,

K. Ramalakshmi,

A. Rex Macedo Arokiaraj

et al.

Computational and Mathematical Methods in Medicine, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 12

Published: July 15, 2021

In a general computational context for biomedical data analysis, DNA sequence classification is crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and of viruses are essential avoid an outbreak like COVID-19. Regardless, the feature selection process remains most challenging aspect issue. The commonly representations worsen case high dimensionality, sequences lack explicit features. It also helps detecting effect drug design. days, deep (DL) models can automatically extract features from input. work, we employed CNN, CNN-LSTM, CNN-Bidirectional LSTM architectures using Label K -mer encoding classification. evaluated on different metrics. From experimental results, CNN with id="M2"> offers accuracy 93.16% 93.13%, respectively, testing data.

Language: Английский

Citations

95
A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection DOI Creative Commons
Sushruta Mishra, Hiren Kumar Thakkar, Pradeep Kumar Mallick

et al.

Sustainable Cities and Society, Journal Year: 2021, Volume and Issue: 72, P. 103079 - 103079

Published: June 9, 2021

A sustainable healthcare focuses on enhancing and restoring public health parameters thereby reducing gloomy impacts social, economic environmental elements of a city. Though it has uplifted health, yet the rise chronic diseases is concern in cities. In this work, lung cancer detection model developed to integrate Internet Health Things (IoHT) computational intelligence, causing least harm environment. IoHT unit retains connectivity continuously generates data from patients. Heuristic Greedy Best First Search (GBFS) algorithm used select most relevant attributes upon which random forest applied classify differentiates affected patients normal ones based detected symptoms. It observed during experiment that GBFS-Random shows promising outcome. While an optimal accuracy 98.8 % was generated, simultaneously, latency 1.16 s noted. Specificity sensitivity recorded with proposed are 97.5 97.8 %, respectively. The mean accuracy, specificity, sensitivity, f-score value 96.96 96.26 96.34 96.32 respectively, over various types datasets implemented. smart intelligent sustainable. reduces unnecessary manual overheads, safe, preserves resources human resources, assists medical professionals quick reliable decision making diagnosis.

Language: Английский

Citations

89
Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System DOI Creative Commons
Abdullah Lakhan, Mazin Abed Mohammed, Ahmed N. Rashid

et al.

Sensors, Journal Year: 2021, Volume and Issue: 21(12), P. 4093 - 4093

Published: June 14, 2021

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it a distributed hospital further study. provides patients with variety paid programmes help them keep track their health problems. However, the current system services are expensive, offloaded in network insecure. research develops new, cost-effective stable framework based on blockchain-enabled fog cloud. study aims reduce cost application as they processing system. devises an different algorithm techniques, such Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Blockchain schemes ensure consistency validation symmetric cryptography. due workflow tasks scheduled other nodes, heterogeneous, earliest finish, time-based scheduling deals execution under deadlines. Simulation results show that proposed outperform all existing baseline approaches terms implementation applications.

Language: Английский

Citations

88
Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion DOI Creative Commons

Rizwana Irfan,

Abdulwahab Ali Almazroi, Hafiz Tayyab Rauf

et al.

Diagnostics, Journal Year: 2021, Volume and Issue: 11(7), P. 1212 - 1212

Published: July 5, 2021

Breast cancer is becoming more dangerous by the day. The death rate in developing countries rapidly increasing. As a result, early detection of breast critical, leading to lower rate. Several researchers have worked on segmentation and classification using various imaging modalities. ultrasonic modality one most cost-effective techniques, with higher sensitivity for diagnosis. proposed study segments lesion images Dilated Semantic Segmentation Network (Di-CNN) combined morphological erosion operation. For feature extraction, we used deep neural network DenseNet201 transfer learning. We propose 24-layer CNN that uses learning-based extraction further validate ensure enriched features target intensity. To classify nodules, vectors obtained from were fused parallel fusion. methods evaluated 10-fold cross-validation vector combinations. accuracy CNN-activated DenseNet201-activated Support Vector Machine (SVM) classifier was 90.11 percent 98.45 percent, respectively. With 98.9 accuracy, version SVM outperformed other algorithms. When compared recent algorithms, algorithm achieves better diagnosis

Language: Английский

Citations

88
Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images DOI Creative Commons
Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Diyar Qader Zeebaree

et al.

Applied Artificial Intelligence, Journal Year: 2021, Volume and Issue: 35(15), P. 2157 - 2203

Published: Dec. 2, 2021

Breast cancer is one of the most prevalent types that plagues females. Mortality from breast could be reduced by diagnosing and identifying it at an early stage. To detect cancer, various imaging modalities can used, such as mammography. Computer-Aided Detection/Diagnosis (CAD) systems assist expert radiologist to diagnose This paper introduces findings a systematic review seeks examine state-of-the-art CAD for detection. based on 118 publications published in 2018–2021 retrieved major scientific publication databases while using rigorous methodology review. We provide general description analysis existing use machine learning methods well their current state mammogram image classification methods. presents all stages including pre-processing, segmentation, feature extraction, selection, classification. identify research gaps outline recommendations future research. may helpful both clinicians, who diagnosis researchers find knowledge create more contributions diagnostics.

Language: Английский

Citations

82
Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion DOI Creative Commons
Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Diyar Qader Zeebaree

et al.

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(24), P. 12122 - 12122

Published: Dec. 20, 2021

Breast cancer detection using mammogram images at an early stage is important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast from images. Hybrid thresholding and machine learning are used to derive region interest (ROI). The derived ROI then separated into five different blocks. wavelet transform applied suppress noise each produced block based on BayesShrink soft by capturing high low frequencies within sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), proposed extract multiple features denoised block. number extracted reduced genetic algorithm. Five classifiers trained with artificial neural network (ANN) classify Lastly, fusion process performed results blocks obtain final decision. approach tested evaluated four benchmark image datasets (MIAS, DDSM, INbreast, BCDR). present single- double-dataset evaluations. Only one dataset training testing single-dataset evaluation, whereas two (one training, testing) evaluation. experiment show that yields better INbreast whilst obtained remaining outperforms other state-of-the-art models Mini-MIAS dataset.

Language: Английский

Citations

82
An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification DOI
Sarmad Maqsood, Robertas Damaševičius, Faisal Mehmood Shah

et al.

Lecture notes in computer science, Journal Year: 2021, Volume and Issue: unknown, P. 105 - 118

Published: Jan. 1, 2021

Language: Английский

Citations

68
A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis DOI Open Access
M. F. Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar

et al.

Cancers, Journal Year: 2021, Volume and Issue: 13(23), P. 6116 - 6116

Published: Dec. 4, 2021

Breast cancer is now the most frequently diagnosed in women, and its percentage gradually increasing. Optimistically, there a good chance of recovery from breast if identified treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency accuracy predicting growth cells utilizing medical imaging modalities. As yet, few review studies on diagnosis are available that summarize some existing studies. However, these were unable to address emerging architectures modalities diagnosis. This focuses evolving deep learning detection. In what follows, this survey presents architectures, analyzes strengths limitations studies, examines used datasets, reviews image pre-processing techniques. Furthermore, concrete diverse modalities, performance metrics results, challenges, research directions future presented.

Language: Английский

Citations

65