Selecting the optimal transfer learning model for precise breast cancer diagnosis utilizing pre-trained deep learning models and histopathology images

Ravikumar, Aswathy; Sriraman, Harini; Saleena, B.; Prakash, B.

doi:10.1007/s12553-023-00772-0

Cited by 3 publications

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multistage transfer learning for medical images

Ayana,

Dese,

Abagaro

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep learning algorithms for optimal performance in medical image analysis. This paper explores the growing demand for precise and robust medical image analysis by focusing on an advanced deep learning technique, multistage transfer learning. Over the past decade, multistage transfer learning has emerged as a pivotal strategy, particularly in overcoming challenges associated with limited medical data and model generalization. However, the absence of well-compiled literature capturing this development remains a notable gap in the field. This exhaustive investigation endeavors to address this gap by providing a foundational understanding of how multistage transfer learning approaches confront the unique challenges posed by insufficient medical image datasets. The paper offers a detailed analysis of various multistage transfer learning types, architectures, methodologies, and strategies deployed in medical image analysis. Additionally, it delves into intrinsic challenges within this framework, providing a comprehensive overview of the current state while outlining potential directions for advancing methodologies in future research. This paper underscores the transformative potential of multistage transfer learning in medical image analysis, providing valuable guidance to researchers and healthcare professionals.

show abstract

Multistage transfer learning for medical images

Ayana,

Dese,

Abagaro

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

Artificial Intelligence-Based Thermal Imaging for Breast Tumor Location and Size Estimation Using Thermal Impedance

Nascimento,

Menegaz,

Guimaraes

2024

ASME Journal of Heat and Mass Transfer

View full text Add to dashboard Cite

Breast cancer has the highest incidence and mortality in women worldwide. Early and accurate detection of the disease is crucial for reducing mortality rates. Tumours can be detected from a temperature gradient due to high vascularization and increased metabolic activity of cancer cells. Thermal infrared images have been recognized as potential alternatives to detect these tumours. However, various pathological processes can produce significant and unpredictable changes in body temperature. These limitations suggest thermal imaging should be used as an adjuvant examination, not a diagnostic test. Another limitation is the low sensitivity to tiny and deep tumours, often found in the analysis of surface temperatures using thermal images. Even the use of artificial intelligence directly on these images has failed to accurately locate and detect the tumour size due to the low sensitivity of temperatures and position within the breast. Thus, we aimed to develop techniques based on applying the thermal impedance method and artificial intelligence to determine the origin of the heat source (abnormal cancer metabolism) and its size. The low sensitivity to tiny and deep tumours is circumvented by utilizing the concept of thermal impedance and artificial intelligence techniques. We describe the development of a thermal model and the creation of a database based on its solution. We also outline the choice of detectable parameters in the thermal image, deep learning libraries, and network training using convolutional neural networks. Lastly, we present tumour location and size estimates based on thermographic images obtained from simulated thermal models of a breast.

show abstract

Data Analytics to Forecast Brain Cancer Risk and Suitable Digital Solutions in India

Ravikumar,

Sriraman,

Pandey

et al. 2024

Advances in Healthcare Information Systems and Administration

View full text Add to dashboard Cite

Brain tumours are among the worst human malignancies. Accurate and dependable segmentation of brain tumours using MRI images aids in treatment planning and extends patients' life spans. To implement the treatment regimen, the tumor present in these slices must be accurately identified. The tumor resembles the various brain tissues that are present. This makes finding brain problems exceedingly difficult. Despite the rising incidence of brain tumors in developing nations like India, few population-based statistics are available. This study looks at the digital tools and IT infrastructure that is available for the early detection of brain tumors. The study examines the connection between the tumor/stroke and India's current infrastructure and facilities for brain tumor identification and treatment. A detailed study of risks and trends associated with a brain tumor in India is given. Using AI approaches, the infrastructural requirements, and budget for enhancing the facilities for digital health facilities for brain tumor treatments are proposed. The study examines the impact of cancer in India, and brain tumor in males and females including children in India. We have examined the burden of cancer in India. All the data comprised from the year 2004 to the year 2022. Yearly registrations of brain tumor cases in hospitals and therapy costs have also been studied and examined in this paper. Shortages of machines in various states and the initiative and funding of the government are also studied. The different machine learning algorithms used for Brain MRI segmentation have been compared. The precise specifications and characteristics of the segmentation challenge will determine the most effective strategy for brain MRI segmentation. To choose the best strategy for a particular application, it is frequently beneficial to compare and assess the performance of various strategies using the right evaluation metrics and validation procedures.

show abstract

Selecting the optimal transfer learning model for precise breast cancer diagnosis utilizing pre-trained deep learning models and histopathology images

Cited by 3 publications

References 89 publications

Multistage transfer learning for medical images

Multistage transfer learning for medical images

Artificial Intelligence-Based Thermal Imaging for Breast Tumor Location and Size Estimation Using Thermal Impedance

Data Analytics to Forecast Brain Cancer Risk and Suitable Digital Solutions in India

Contact Info

Product

Resources

About