An Explainable AI System for Medical Image Segmentation With Preserved Local Resolution: Mammogram Tumor Segmentation DOI Creative Commons
Aya Farrag, Gad Gad, Zubair Md. Fadlullah

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 125543 - 125561

Published: Jan. 1, 2023

Medical image segmentation aims to identify important or suspicious regions within medical images. However, many challenges are usually faced while developing networks for this type of analysis. First, preserving the original resolution is utmost importance task where identifying subtle features abnormalities can significantly impact accuracy diagnosis. The introduction dilated convolution module helped preserve in deep convolutional neural networks, but it has a drawback: loss local spatial due increased kernel sparsity checkboard patterns. To address this, work, double-dilated proposed maintain achieving large receptive field. This approach applied tumor breast cancer mammograms as proof-of-concept. Additionally, study tackles issue pixel-level class imbalance mammogram screenings by comparing various functions find best one mass segmentation. Our work also addresses "black-box" nature models performing quantitative and qualitative evaluations their interpretability using Gradient weighted Class Activation Map (Grad-CAM) with other explainable An experimental analysis on lesion performed from INBreast dataset, both before after integrating dilation into state-of-the-art network. results demonstrate effectiveness terms Dice similarity Miss Detection rate promotes Tversky Loss function training pixel-imbalanced data Grad-CAM explaining results.

Language: Английский

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment DOI Open Access
Narendra N. Khanna, Mahesh Maindarkar, Vijay Viswanathan

et al.

Healthcare, Journal Year: 2022, Volume and Issue: 10(12), P. 2493 - 2493

Published: Dec. 9, 2022

: The price of medical treatment continues to rise due (i) an increasing population; (ii) aging human growth; (iii) disease prevalence; (iv) a in the frequency patients that utilize health care services; and (v) increase price.

Language: Английский

Citations

162
Radiogenomics: a key component of precision cancer medicine DOI
Zaoqu Liu,

Tian Duan,

Yuyuan Zhang

et al.

British Journal of Cancer, Journal Year: 2023, Volume and Issue: 129(5), P. 741 - 753

Published: July 6, 2023

Language: Английский

Citations

45
Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review DOI

Manasvi Singh,

Ashish Kumar,

Narendra N. Khanna

et al.

EClinicalMedicine, Journal Year: 2024, Volume and Issue: 73, P. 102660 - 102660

Published: May 27, 2024

Language: Английский

Citations

28
BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification DOI Creative Commons
Muhammad Sami Ullah, Muhammad Attique Khan, Nouf Abdullah Almujally

et al.

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

Published: March 11, 2024

Abstract A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of most deep architecture classification (ii) an expert field who can assess output models. These difficulties motivate us to propose efficient and accurate system based on evolutionary optimization four types modalities (t1 tumor, t1ce t2 flair tumor) large-scale MRI database. Thus, CNN modified domain knowledge connected with algorithm select hyperparameters. In parallel, Stack Encoder–Decoder network designed ten convolutional layers. The features both models are extracted optimized improved version Grey Wolf updated criteria Jaya algorithm. speeds up process improves accuracy. Finally, selected fused novel parallel pooling approach that classified neural networks. Two datasets, BraTS2020 BraTS2021, have been employed experimental tasks obtained average accuracy 98% maximum single-classifier 99%. Comparison also conducted several classifiers, techniques, nets; proposed method achieved performance.

Language: Английский

Citations

17
UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias DOI Creative Commons
Jasjit S. Suri, Mrinalini Bhagawati, Sushant Agarwal

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 11, P. 595 - 645

Published: Dec. 26, 2022

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and contrast. Conventional methods lack accurate automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This first review of its kind that microscopically addressed types by complexity, stratification components, addressing vascular vs. non-vascular framework, key challenge UNet-based architecture, finally interfacing three facets AI, pruning, explainable AI (XAI), AI-bias. PRISMA was used select 267 studies. Five classes were identified labeled as conventional UNet, superior attention-channel hybrid ensemble UNet. We discovered 81 considering six kinds namely encoder, decoder, skip connection, bridge network, loss function, their combination. Vascular architecture compared. AP(ai)Bias 2.0-UNet these based on (i) attributes performance, (ii) and, (iii) pruning (compression). bias such ranking, radial, regional area, (iv) PROBAST, (v) ROBINS-I applied compared using a Venn diagram. systems with sUNet attention. Most studies suffered from low interest XAI strategies. None models qualified be bias-free. There need move paper-to-practice paradigms for clinical evaluation settings.

Language: Английский

Citations

48
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation DOI
Sanjay Saxena, Biswajit Jena,

Bibhabasu Mohapatra

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 153, P. 106492 - 106492

Published: Jan. 4, 2023

Language: Английский

Citations

30
Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification DOI Open Access
Nazik Alturki, Muhammad Umer, Abid Ishaq

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(6), P. 1767 - 1767

Published: March 14, 2023

Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain depends on their early detection. This research work makes use 13 features with a voting classifier that combines logistic regression stochastic gradient descent using extracted by deep convolutional layers for efficient classification tumorous victims from normal. From first second-order tumor features, model training. Using helps to increase precision non-tumor patient classification. proposed along convoluted produces results show highest accuracy 99.9%. Compared cutting-edge methods, approach has demonstrated improved accuracy.

Language: Английский

Citations

28
Dual Deep CNN for Tumor Brain Classification DOI Creative Commons
Aya M. Al‐Zoghby,

Esraa Mohamed K. Al-Awadly,

Ahmad Moawad

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(12), P. 2050 - 2050

Published: June 13, 2023

Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection identification of type location are crucial for effective treatment saving lives. Manual diagnoses time-consuming depend on radiologist experts; the increasing number new cases brain tumors makes it difficult to process massive large amounts data rapidly, as time critical factor in patients' Hence, artificial intelligence (AI) vital understanding its various types. Several studies proposed different techniques BT classification. These machine learning (ML) deep (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior self-learning robust classification recognition tasks. This research focuses classifying three types using MRI imaging: meningioma, glioma, pituitary tumors. DCTN model depends dual convolutional neural networks with VGG-16 architecture concatenated custom CNN (convolutional networks) architecture. After conducting approximately 22 experiments architectures models, our reached 100% accuracy during training 99% testing. methodology obtained highest possible improvement over existing studies. solution provides revolution healthcare providers can be used future save human

Language: Английский

Citations

26
Enhancing image classification using adaptive convolutional autoencoder-based snow avalanches algorithm DOI

E. Dhiravidachelvi,

T. Joshva Devadas,

P. Sathish Kumar

et al.

Signal Image and Video Processing, Journal Year: 2024, Volume and Issue: 18(10), P. 6867 - 6879

Published: June 22, 2024

Language: Английский

Citations

10
Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework DOI
Sanjay Saxena,

Aaditya Agrawal,

Pankaj Kumar

et al.

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(18), P. 13647 - 13663

Published: March 17, 2023

Language: Английский

Citations

17