An Open IoHT-Based Deep Learning Framework for Online Medical Image Recognition

Systems developed to work with computational intelligence have become very efficient, and in some cases obtain more accurate results than evaluations by humans. Hence, this work proposes a new online approach based on deep learning tools according to the concept of transfer learning to generate a computational intelligence framework for use with the Internet of Health Things (IoHT) devices. This framework allows the user to add their images and perform platform training almost as easily as creating folders and placing files in regular cloud storage services. The trials carried out with the tool showed that even people with no programming and image processing knowledge were able to set up projects in a few minutes. The proposed approach is validated using three medical databases, which include cerebral vascular accident images for stroke type classification, lung nodule images for malignant classification, and skin images for the classification of melanocytic lesions. The results show the efficiency and reliability of the framework, which reached 91.6% Accuracy in the stroke images and lung nodules databases, and 92% Accuracy in the skin images databases. This prove the immense contribution that this work can bring to assist medical professionals in analyzing complex examinations quickly and accurately, allowing a large medical examination database through a consolidated collaborative IoT platform.

Automated summary

The research proposes a new online approach based on deep learning tools and transfer learning concept to generate a computational intelligence framework for use with Internet of Health Things devices. The efficiency and reliability of the framework is validated using three medical databases, showing high classification accuracy.