Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity DOI

N. Banupriya,

T. Sethukarasi

Technology and Health Care, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 4, 2024

Breast cancer results from an uncontrolled growth of breast tissue. Many methods diagnosis are using multi-omics data to better understand the complexity cancer. The new strategy laid out in this work, called “Hybrid-OmniSeq,” is a deep learning-based analysis technology that uses molecular subtypes gene increase precision and effectiveness diagnosis. For preprocessing, BC-VM procedure utilized, for subtype analysis, BC-MSA utilized. implementation Deep Neural Network (DNN) conjunction with Sequential Forward Floating Selection (SFFS) Truncated Singular Value Decomposition (TSVD) entropy enable adaptive learning data. Five machine classifiers used classification purpose. Hybrid-OmniSeq variety thorough analytical process achieve remarkable diagnostic accuracy. Learning-based sequential approach was evaluated METABRIC RNA-seq sets intrinsic According test results, Logistic Regression (LR) had ER (Estrogen Receptor) status values 94.51%, 96.33%, HER2 (Human Epidermal factor 92.3%; Random Forest (RF) 93.77%, 95.23%, 93.4%. LR RF detection accuracy all when compared alternative or majority voting method, providing comprehensive understanding underlying causes

Language: Английский

Breast cancer classification based on hybrid CNN with LSTM model DOI Creative Commons
Mourad Kaddes,

Yasser M. Ayid,

Ahmed M. Elshewey

et al.

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

Published: Feb. 5, 2025

Breast cancer (BC) is a global problem, largely due to shortage of knowledge and early detection. The speed-up process detection classification crucial for effective treatment. Medical image analysis methods computer-aided diagnosis can enhance this process, providing training assistance less experienced clinicians. Deep Learning (DL) models play great role in accurately detecting classifying the huge dataset, especially when dealing with large medical images. This paper presents novel hybrid model DL combined Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) binary breast on two datasets available at Kaggle repository. CNNs extract mammographic features, including spatial hierarchies malignancy patterns, whereas LSTM networks characterize sequential dependencies temporal interactions. Our method combines these structures improve accuracy resilience. We compared proposed other models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, RESNET-50. CNN-LSTM achieved superior performance accuracies 99.17% 99.90% respective datasets. uses prediction evaluation metrics accuracy, sensitivity, specificity, F-score, AUC curve. results showed that our classifiers others second dataset.

Language: Английский

Citations

2
A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics DOI
Hari Mohan, Joon Yoo

Journal of Cancer Research and Clinical Oncology, Journal Year: 2023, Volume and Issue: 149(15), P. 14365 - 14408

Published: Aug. 4, 2023

Language: Английский

Citations

22
DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image DOI
Chengzhang Zhu, Xian Chai, Zhiyuan Wang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 93, P. 106221 - 106221

Published: March 18, 2024

Language: Английский

Citations

6
Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis DOI Creative Commons
Mugahed A. Al-antari

Diagnostics, Journal Year: 2024, Volume and Issue: 14(12), P. 1265 - 1265

Published: June 15, 2024

Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy efficiency disease detection, classification, treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, presents a curated collection cutting-edge research that explores integration AI ML technologies into various diagnostic modalities. The contributions presented here highlight innovative algorithms, models, applications pave way improved capabilities across range medical fields, including radiology, pathology, genomics, personalized medicine. By showcasing both theoretical practical implementations, this Issue aims to provide comprehensive overview current trends future directions AI-driven fostering further collaboration dynamic impactful area healthcare. We have published total 12 articles all collected between March 2023 December 2023, comprising 1 Editorial cover letter, 9 regular articles, review article, article categorized as “other”.

Language: Английский

Citations

5
XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer DOI Creative Commons
Parvathaneni Naga Srinivasu,

G. Jaya Lakshmi,

Abhishek Gudipalli

et al.

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

Published: Nov. 19, 2024

Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, women's health. The present study outlines a novel approach for analyzing data by using CatBoost classification model with multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere proposed MLP model. aims enhance interpretability predictions leveraging benefits technique feature identification and also contributing towards decision CatBoost+MLP has been evaluated Shapley additive explanations values analyze significance decision-making. Initially, engineering done analysis variance identify significant features. alone being analyzed divergent performance metrics, results obtained compared contemporary techniques.

Language: Английский

Citations

4
Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques DOI
Hari Mohan, Joon Yoo, Serhii Dashkevych

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

Citations

0
LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection DOI Creative Commons
Hari Mohan, Joon Yoo, Saurabh Agarwal

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(1), P. 73 - 73

Published: Jan. 15, 2025

Breast cancer ranks as the second most prevalent globally and is frequently diagnosed among women; therefore, early, automated, precise detection essential. Most AI-based techniques for breast are complex have high computational costs. Hence, to overcome this challenge, we presented innovative LightweightUNet hybrid deep learning (DL) classifier accurate classification of cancer. The proposed model boasts a low cost due its smaller number layers in architecture, adaptive nature stems from use depth-wise separable convolution. We employed multimodal approach validate model’s performance, using 13,000 images two distinct modalities: mammogram imaging (MGI) ultrasound (USI). collected datasets seven different sources, including benchmark DDSM, MIAS, INbreast, BrEaST, BUSI, Thammasat, HMSS. Since various resized them uniform size 256 × pixels normalized Box-Cox transformation technique. USI dataset smaller, applied StyleGAN3 generate 10,000 synthetic images. In work, performed separate experiments: first on real without augmentation + GAN-augmented our method. During experiments, used 5-fold cross-validation method, obtained good results (87.16% precision, 86.87% recall, 86.84% F1-score, accuracy) adding any extra data. Similarly, experiment provides better performance (96.36% 96.35% accuracy). This approach, which utilizes LightweightUNet, enhances by 9.20% 9.48% 9.51% increase accuracy combined dataset. works very well thanks creative network design, fake data, training These show that has lot potential clinical settings.

Language: Английский

Citations

0
Ensemble Machine Learning Algorithms for Precision Breast Cancer Diagnosis: A Multi-criteria Evaluation Approach DOI

Srinivasa Rao Pallapu,

Syed Khasim

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Feb. 11, 2025

Language: Английский

Citations

0
Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images DOI

Jagendra Singh,

Nazeer Shaik,

Dinesh Prasad Sahu

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 455 - 466

Published: Jan. 1, 2025

Language: Английский

Citations

0
Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis DOI
Hanaa ZainEldin, Amna Bamaqa,

Mohammed Farsi

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 109, P. 108041 - 108041

Published: May 22, 2025

Language: Английский

Citations

0