Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods DOI Creative Commons

Zobeda Hatif Naji Al-azzwi,

Alexey Nazarov

Asian Pacific Journal of Cancer Prevention, Journal Year: 2023, Volume and Issue: 24(6), P. 2141 - 2148

Published: June 1, 2023

Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying classifying brain tumors. For the diagnosis treatment of cancer diseases, classification accuracy are crucial. The aim this study was to improve ensemble deep learning models classifing tumor increase performance structure by combining different model develop a with more accurate predictions than individual models.Convolutional neural networks (CNNs), which made up single algorithm called CNN model, foundation most current methods illness images. combined create method. However, compared machine algorithm, accurate. This used stacked technology. data set obtained from Kaggle included two categories: abnormal & normal brains. trained three models: VGG19, Inception v3, Resnet 10.The 96.6% binary (0,1) have been achieved Loss cross entropy, Adam optimizer take into consideration stacking models.The can be improved over framework.

Language: Английский

A Deep Learning Model for Early Prediction of Pneumonia Using VGG19 and Neural Networks DOI
Shagun Sharma, Kalpna Guleria

Lecture notes in networks and systems, Journal Year: 2023, Volume and Issue: unknown, P. 597 - 612

Published: Jan. 1, 2023

Language: Английский

Citations

46
Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques DOI Creative Commons
Javier Civit-Masot,

Alejandro Bañuls-Beaterio,

Manuel Domínguez-Morales

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2022, Volume and Issue: 226, P. 107108 - 107108

Published: Sept. 7, 2022

Lung cancer has the highest mortality rate in world, twice as high second highest. On other hand, pathologists are overworked and this is detrimental to time spent on each patient, diagnostic turnaround time, their success rate.In work, we design, implement, evaluate a aid system for non-small cell lung detection, using Deep Learning techniques.The classifier developed based Artificial Intelligence techniques, obtaining an automatic classification result between healthy, adenocarcinoma squamous carcinoma, given histopathological image from tissue. Moreover, report module Explainable techniques included gives pathologist information about image's areas used classify sample confidence of belonging class.The results show accuracy 97.11 99.69%, depending number classes classified, value area under ROC curve 99.77 99.94%.The obtain substantial improvement according previous works. Thanks report, by can be reduced.

Language: Английский

Citations

48
An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays DOI
Rishav Pramanik, S. Sarkar, Ram Sarkar

et al.

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 128, P. 109464 - 109464

Published: Aug. 10, 2022

Language: Английский

Citations

41
RETRACTED ARTICLE: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm DOI Open Access
Abobaker Mohammed Qasem Farhan,

Shangming Yang

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 82(25), P. 38561 - 38587

Published: March 22, 2023

Language: Английский

Citations

41
Computer aided diagnosis using Harris Hawks optimizer with deep learning for pneumonia detection on chest X-ray images DOI
V. Parthasarathy, S. Saravanan

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(3), P. 1677 - 1683

Published: Jan. 18, 2024

Language: Английский

Citations

15
A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble DOI Creative Commons
Qiuyu An, Wei Chen, Wei Shao

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 390 - 390

Published: Feb. 11, 2024

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in efficacy medical decision systems. This paper presents novel approach utilizing convolutional neural network that effectively amalgamates strengths EfficientNetB0 and DenseNet121, it is enhanced by suite attention mechanisms for refined classification. Leveraging pre-trained models, our employs multi-head, self-attention modules meticulous feature extraction from images. The model’s integration processing efficiency are further augmented channel-attention-based fusion strategy, one complemented residual block an attention-augmented enhancement dynamic pooling strategy. Our used dataset, which comprises comprehensive collection chest images, represents both healthy individuals those affected pneumonia, serves as foundation this research. study delves into algorithms, architectural details, operational intricacies proposed model. empirical outcomes model noteworthy, with exceptional performance marked accuracy 95.19%, precision 98.38%, recall 93.84%, F1 score 96.06%, specificity 97.43%, AUC 0.9564 on test dataset. These results not only affirm high diagnostic accuracy, but also highlight its promising potential real-world clinical deployment.

Language: Английский

Citations

14
Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model DOI Creative Commons
Mudasir Ali, Mobeen Shahroz,

Urooj Akram

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 34691 - 34707

Published: Jan. 1, 2024

Pneumonia is a potentially life-threatening infectious disease that typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds, or lung biopsies. Accurate diagnosis crucial wrong diagnosis, inadequate treatment lack of can cause serious consequences for patients may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts diagnosing pneumonia by assisting their decision-making process. By leveraging models, healthcare professionals enhance accuracy make informed decisions suspected having pneumonia. In this study, six models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50, Efficient-NetV2L are implemented evaluated. study also incorporates the Adam optimizer, which effectively adjusts epoch all models. trained on dataset 5856 X-ray images show 87.78%, 88.94%, 90.7%, 91.66%, 87.98%, 94.02% ResNet50 EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates highest proves its robustness detection. These findings highlight potential accurately detecting predicting based images, providing valuable support clinical improving patient treatment.

Language: Английский

Citations

13
Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey DOI Creative Commons
Raheel Siddiqi, Sameena Javaid

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(8), P. 176 - 176

Published: July 23, 2024

This paper addresses the significant problem of identifying relevant background and contextual literature related to deep learning (DL) as an evolving technology in order provide a comprehensive analysis application DL specific pneumonia detection via chest X-ray (CXR) imaging, which is most common cost-effective imaging technique available worldwide for diagnosis. particular key period associated with COVID-19, 2020–2023, explain, analyze, systematically evaluate limitations approaches determine their relative levels effectiveness. The context applied both aid automated substitute existing expert radiography professionals, who often have limited availability, elaborated detail. rationale undertaken research provided, along justification resources adopted relevance. explanatory text subsequent analyses are intended sufficient detail being addressed, solutions, these, ranging from more general. Indeed, our evaluation agree generally held view that use transformers, specifically, vision transformers (ViTs), promising obtaining further effective results area using CXR images. However, ViTs require extensive address several limitations, specifically following: biased datasets, data code ease model can be explained, systematic methods accurate comparison, notion class imbalance possibility adversarial attacks, latter remains fundamental research.

Language: Английский

Citations

13
X-ray image-based pneumonia detection and classification using deep learning DOI
Nigus Wereta Asnake, Ayodeji Olalekan Salau, Aleka Melese Ayalew

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(21), P. 60789 - 60807

Published: Jan. 5, 2024

Language: Английский

Citations

11
Medical imaging: A Critical Review on X-ray Imaging for the Detection of Infection DOI

Egwonor Loveth Irede,

Omowunmi Rebecca Aworinde,

Ogunnaike Korede Lekan

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: July 15, 2024

Medical imaging is critical in detecting and managing infections, with X-ray being a cornerstone this domain. This review provides an overview of principles, including the basic mechanisms underlying absorption image formation. Emerging trends for various such as respiratory bone joint fungal viral are discussed detail. Some advantages over other techniques infection detection highlighted, along recent technological advancements techniques. Key topics covered include film-screen radiography, computed flat-panel detector-based evolution thin-film transistor array-based digital radiography. Additionally, principles direct indirect conversion detectors explored, primary physical parameters spatial resolution, contrast, noise, modulation transfer function (MTF), detective quantum efficiency (DQE). Furthermore, application tomography (CT) 3D radiography role microscopy discussed. Clinical implications elaborated, its early diagnosis, assessment disease progression, identification complications, guidance interventional procedures, screening high-risk populations. Recommendations optimizing clinical practice suggestions future research directions provided. In summary, offers insights into current state detection, highlighting significance research.

Language: Английский

Citations

10