Diagnosis of skin diseases using Convolutional Neural Networks

机译：使用卷积神经网络诊断皮肤疾病

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Dermatology is one of the most unpredictable and difficult terrains to diagnose due its complexity. In the field of dermatology, many a times extensive tests are to be carried out so as to decide upon the skin condition the patient may be facing. The time may vary from practitioner to practitioner. This is also based on the experience of that person too. So, there is a need of a system which can diagnose the skin diseases without any of these constraints. We propose an automated image based system for recognition of skin diseases using machine learning classification. This system will utilize computational technique to analyze, process, and relegate the image data predicated on various features of the images. Skin images are filtered to remove unwanted noise and also process it for enhancement of the image. Feature extraction using complex techniques such as Convolutional Neural Network (CNN), classify the image based on the algorithm of softmax classifier and obtain the diagnosis report as an output. This system will give more accuracy and will generate results faster than the traditional method, making this application an efficient and dependable system for dermatological disease detection. Furthermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream.

机译：由于皮肤病的复杂性，皮肤病是最不可预测和最困难的地形之一。在皮肤病学领域，许多次要进行广泛的测试，以便确定患者可能面对的皮肤状况。时间可能因从业者而异。这也基于该人的经验。因此，需要一种能够在没有任何这些约束的情况下诊断皮肤疾病的系统。我们提出了一种基于图像的自动系统，用于使用机器学习分类识别皮肤疾病。该系统将利用计算技术来分析，处理和发布基于图像各种特征的图像数据。皮肤图像经过过滤以去除不需要的噪声，并对其进行处理以增强图像。使用卷积神经网络（CNN）等复杂技术进行特征提取，基于softmax分类器算法对图像进行分类，并获得诊断报告作为输出。与传统方法相比，该系统将提供更高的准确性并更快地产生结果，从而使该应用程序成为用于皮肤病学疾病检测的高效且可靠的系统。此外，它还可用作皮肤科专业医学生的可靠实时教学工具。

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来源
《2018 Second International Conference on Electronics, Communication and Aerospace Technology》|2018年|1048-1051|共4页
会议地点 Coimbatore(IN)
作者
Jainesh Rathod; Vishal Wazhmode; Aniruddh Sodha; Praseniit Bhavathankar;
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作者单位

Department Of Information Technology, Sardar Patel Institute Of Technology, Mumbai;

Department Of Information Technology, Sardar Patel Institute Of Technology, Mumbai;

Department Of Information Technology, Sardar Patel Institute Of Technology, Mumbai;

Department Of Information Technology, Sardar Patel Institute Of Technology, Mumbai;

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原文格式 PDF
正文语种 eng
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关键词
Skin; Diseases; Convolutional neural networks; Feature extraction; Conferences; Machine learning; Convolution;

机译：皮肤;疾病;卷积神经网络;特征提取;会议;机器学习;卷积;;

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1. A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks [J] . Samuel Akyeramfo-Sam, Acheampong Addo Philip, Derrick Yeboah, International Journal of Information Technology and Computer Science . 2019,第11期

机译：基于卷积神经网络的基于Web的皮肤疾病诊断
2. Automatic diagnosis of skin diseases using convolution neural network [J] . Shanthi T., Sabeenian R. S., Anand R. Microprocessors and microsystems . 2020,第Jula期

机译：卷积神经网络自动诊断皮肤病
3. DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis [J] . Gonzalez-Diaz Ivan Biomedical and Health Informatics, IEEE Journal of . 2019,第2期

机译：DermaKNet：将皮肤科医生的知识整合到卷积神经网络中以进行皮肤病变诊断
4. Diagnosis of skin diseases using Convolutional Neural Networks [C] . Jainesh Rathod, Vishal Waghmode, Aniruddh Sodha, International Conference on Electronics, Communication and Aerospace Technology . 2018

机译：利用卷积神经网络诊断皮肤病
5. Cloud-Based Skin Lesion Diagnosis System Using Convolutional Neural Networks [D] . Akar, Esad. 2018

机译：卷积神经网络基于云的皮肤病变诊断系统
6. Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic [O] . Sneha Kugunavar, C. J. Prabhakar 2021

机译：锥形神经网络用于冠状病毒疾病大流行病的诊断和预后
7. A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks [O] . Samuel Akyeramfo-Sam, Acheampong Addo Philip, Derrick Yeboah, 2019

机译：使用卷积神经网络的基于Web的皮肤病诊断

Diagnosis of skin diseases using Convolutional Neural Networks

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