Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models DOI Creative Commons

Reem Jalloul,

Chethan Hasigala Krishnappa,

Victor Ikechukwu Agughasi

и другие.

Technologies, Год журнала: 2024, Номер 13(1), С. 7 - 7

Опубликована: Дек. 26, 2024

Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early by capturing subtle thermal variations indicative abnormalities. This study investigates compares performance various deep learning machine models analyzing thermographic data to classify tissue healthy, benign, or malignant. To maximize accuracy, preprocessing, feature extraction, dimensionality reduction were implemented isolate distinguishing characteristics across types. Leveraging advanced extraction visualization techniques inspired geospatial methodologies, we evaluated several architectures classical classifiers using DRM-IR Thermography Mendeley datasets. Among tested models, ResNet152 architecture combined Support Vector Machine (SVM) classifier delivered highest performance, achieving 97.62% 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score 97.16%, area under curve (AUC) 99%, latency 0.06 s, CPU utilization 88.66%. These findings underscore potential integrating infrared approaches significantly improve efficiency detection, supporting its role valuable tool diagnosis.

Язык: Английский

Effective Approach for Fine-Tuning Pre-Trained Models for the Extraction of Texts From Source Codes DOI Creative Commons

D. Shruthi,

H. K. Chethan,

Victor Ikechukwu Agughasi

и другие.

ITM Web of Conferences, Год журнала: 2024, Номер 65, С. 03004 - 03004

Опубликована: Янв. 1, 2024

This study introduces SR-Text, a robust approach leveraging pre-trained models like BERT and T5 for enhanced text extraction from source codes. Addressing the limitations of traditional manual summarization, our methodology focuses on fine-tuning these to better understand generate contextual summaries, thus overcoming challenges such as long-term dependency dataset quality issues. We conduct detailed analysis programming language syntax semantics develop syntax-aware retrieval techniques, significantly boosting accuracy relevance texts extracted. The paper also explores hybrid that integrates statistical machine learning with rule-based methods, enhancing robustness adaptability processes across diverse coding styles languages. Empirical results meticulously curated demonstrate marked improvements in performance metrics: precision increased by 15%, recall 20%, an F1 score enhancement 18%. These underscore effectiveness using advanced software engineering tasks. research not only paves way future work multilingual code summarization but discusses broader implications automated tools, proposing directions further refine expand this methodology.

Язык: Английский

Процитировано

0
Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models DOI Creative Commons

Reem Jalloul,

Chethan Hasigala Krishnappa,

Victor Ikechukwu Agughasi

и другие.

Technologies, Год журнала: 2024, Номер 13(1), С. 7 - 7

Опубликована: Дек. 26, 2024

Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early by capturing subtle thermal variations indicative abnormalities. This study investigates compares performance various deep learning machine models analyzing thermographic data to classify tissue healthy, benign, or malignant. To maximize accuracy, preprocessing, feature extraction, dimensionality reduction were implemented isolate distinguishing characteristics across types. Leveraging advanced extraction visualization techniques inspired geospatial methodologies, we evaluated several architectures classical classifiers using DRM-IR Thermography Mendeley datasets. Among tested models, ResNet152 architecture combined Support Vector Machine (SVM) classifier delivered highest performance, achieving 97.62% 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score 97.16%, area under curve (AUC) 99%, latency 0.06 s, CPU utilization 88.66%. These findings underscore potential integrating infrared approaches significantly improve efficiency detection, supporting its role valuable tool diagnosis.

Язык: Английский

Процитировано

0