International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 06 | Jun 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
DETECTION OF DIFFERENT TYPES OF SKIN DISEASES USING RASPBERRY PI J.S.Leena jasmine.[1], Rutheeswaran.k[2], Sarath. H[3], Mohan Kumar. K [4], Praveen Kumar.M.S[5] Professor, Department of ECE, Velammal Engineering College, Chennai, Tamil Nadu [1] UG Scholars [2,3,4] , Department of ECE, Velammal Engineering College, Chennai, Tamil Nadu -------------------------------------------------------------------------***----------------------------------------------------------------------
Abstract- To diagnosing the different types of skin
may result in discolored patches on the skin. The discolored patches usually increase with time [9]. The disease can affect the skin on any part of the body. This disease is non-fatal but it will affect the patient’s mental state. A Convolutional Neural network or CNN is a very popular Machine-learning algorithm in the area of computer vision. Training time and the amount of data required for CNN are more than traditional machine learning algorithms. People develop CNN with great performance and share the details to use in the future. Interesting use of a convolutional neural network is that this pre-trained network can be reused for a related task in another new task [5]. Humans can transfer knowledge learned from one task. The new task will not be learned from scratch instead previously acquired knowledge would use to learn a new task. Transfer learning is a very popular approach in recent days [9]. It is a process of using gained knowledge of one network on other related problems. It is used in a situation where your dataset has too little data to train a model from first. The process of learning knowledge and applying it to a similar and different problem is called Transfer learning. There are two steps to be followed Pre-training, Fine Tuning. Pre-Training: In this process, the training of the network will do with a large data set [1]-[3]. All the parameters of the network are trained and, in this way, the model is learned. Fine Tuning: The new dataset can give to fine-tune the pre-trained CNN. The new data set may be similar or different from the original data set. If the new data set is similar and the size of the data set is, less then only the final layer of the model will train to keep all other layers fixed to avoid overfitting. If the size of the data set is big then the whole model may be retrained with the initial weights of the pre-trained model. If the new data set is not the same as the old data set, then if the size of the data set is less the initial layer can be fixed and the remaining layer can be retrained. In the case where the size of the data set is big, the whole network can be retrained with the initial weight of the pre-trained network [6]. The Internet of things (IoT) is an everlastingly growing network spanning a gathering of devices, sensors, actuators, and even smartphones connected to the internet. These devices sense and assemble data for combination and analysis for better decisions applications. The system of the Internet of Things (IoT) contains an extensive diversity of heterogeneous devices, reaching from wireless sensors to smart home uses connected to the global IP network, and is predictable to include many other types of technologies that are classically not connected so far. The interactive devices in IoT, forced in terms of memory and power, must agree on different
diseases is an exigent process even for better-experienced skin doctors, while there are several reasons for an increase in such conflicts, foremost among them is the reduction of these types of diseases. This paper proposed Deep learning. Nowadays diagnosing diseases through modern technology becomes easy to access and convenient. Due to the emergence of smartphone analysis and providing results in less time. This system will utilize computational techniques to analyze, process, and relegate the image data predicated on various features of the images. Skin images are filtered to remove unwanted noise and process it for enhancement of the image. Using a machine-learning algorithm (Convolutional Neural network) can predict the type of disease and show in the output to the IOT page of the predicted disease. KEYWORDS: IoT Protocol, Skin diseases classification images, Wi-Fi Image Processing, Noise Removal in the given image
1. INTRODUCTION: Medical Image Processing is used widely in the diagnosis of various diseases. It can use to identify various types of skin diseases. In the current work transfer, learning was used to identify the skin diseases melanoma, vitiligo, and vascular tumors. Melanoma is a skin cancer that occurs from the pigment-producing cells, melanocytes [7]. Most melanomas are asymmetrical with an uneven border. The color of melanoma may have a different shade of brown. A small pencil eraser-shaped lesion is the warning sign for melanoma.[4] It is not easy for patients to recognize melanoma. Melanoma is a serious type of skin cancer. Diagnosed timely and treated suitably, it can very often cure with comparatively small surgery alone [8]. An avascular tumor is a tumor that occurs at the vascular origin. It is a growth of soft tissue that can be either begin or malignant. It developed from blood vessels or lymph vessels. It is a massive and complex type of lesion, especially for doctors with no or little experience in this field. Vascular tumors can happen in any part of the body and can be of type being (not cancer) or malignant (cancer). They may form on the skin, in the tissues below the skin, and/or in an organ [10]. They may be found in the tissues below the skin or on the skin. Vitiligo is a disease in which the skin loses its color because of the loss of its pigment cells (melanocytes). Melanocytes are responsible for the color of our skin. Loss of melanocytes
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