Healthcare Informatics & Health Information Technology (SIG Health)

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Paper Type

Complete

Paper Number

1727

Description

The application of Convolutional Neural Networks (CNNs) for medical image classification and segmentation tasks is well-known and highly utilized in deep learning architecture. Digital transformation of healthcare services and technology has presented huge opportunity for new technologies to be investigated. However, analysis of these CNN models for task-specific selection presents an opportunity for additional research. Further, given the plethora of cloud computing-based services, the computational cost has become a crucial factor for model selection. In this paper, we compare the state-of-the-art CNN models in terms of accuracy and cost. A two-stage adaptive transfer learning model framework is designed based on Design Science principles. Our experimental results show that the ResNet50 CNN model has performed well and yielded 80.26% validation accuracy and 47.13% validation loss. The framework could be useful as a decision support tool for medical professionals in medical image classification.

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Aug 9th, 12:00 AM

Comparative study of CNN models for brain tumor classification: Computational efficiency versus accuracy

The application of Convolutional Neural Networks (CNNs) for medical image classification and segmentation tasks is well-known and highly utilized in deep learning architecture. Digital transformation of healthcare services and technology has presented huge opportunity for new technologies to be investigated. However, analysis of these CNN models for task-specific selection presents an opportunity for additional research. Further, given the plethora of cloud computing-based services, the computational cost has become a crucial factor for model selection. In this paper, we compare the state-of-the-art CNN models in terms of accuracy and cost. A two-stage adaptive transfer learning model framework is designed based on Design Science principles. Our experimental results show that the ResNet50 CNN model has performed well and yielded 80.26% validation accuracy and 47.13% validation loss. The framework could be useful as a decision support tool for medical professionals in medical image classification.

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