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An Optimized Intelligent Dermatologic Disease Classification Framework Based on IoT

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Book cover Intelligent Computing: Image Processing Based Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1157))

Abstract

Internet of things (IoT) is one of the recent concepts that provide many services by exploiting the computational power of several devices. One of the emerging applications of the IoT-based technologies can be observed in the field of automated health care and diagnostics. With the help of IoT-based infrastructures, continuous data collection and monitoring are simpler. In most of the scenarios, collected data are massive, unstructured, and contain many redundant parts. It is always a challenging task to find an intelligent way to mine some useful information from a massive dataset with stipulated computing resources. Different types of sensors can be used to acquire data in real time. Some sensors can be body-worn sensors, and some sensors can be placed some distance apart from the body. In dermatological disease detection and classification problem, images of the infected region play a vital role. In this work, an optimized classification method is proposed that can be useful in performing automated classification job in the limited infrastructure of the IoT environment. The input features are optimized in such a way so that it can be useful in faster and accurate classification by the classifier that makes the system intelligent and optimized. Moreover, the hybrid classifier is optimized using different metaheuristic optimization methods for better convergence. The proposed work can be highly beneficial in exploring and applying the power of IoT in the healthcare industry. It is a small step toward the next-generation healthcare systems which can produce faster and accurate results at affordable cost with the help of IoT and remote healthcare monitoring.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, India

    Shouvik Chakraborty & Kalyani Mali

  2. Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, 700160, India

    Sankhadeep Chatterjee

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  1. Shouvik Chakraborty
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  2. Sankhadeep Chatterjee
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  3. Kalyani Mali
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Correspondence to Sankhadeep Chatterjee .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Electronics and Communication Engineering, University of Engineering and Management, Kolkata, West Bengal, India

    Soumen Banerjee

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Chakraborty, S., Chatterjee, S., Mali, K. (2020). An Optimized Intelligent Dermatologic Disease Classification Framework Based on IoT. In: Mandal, J., Banerjee, S. (eds) Intelligent Computing: Image Processing Based Applications. Advances in Intelligent Systems and Computing, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-4288-6_9

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