Skin disease classification

ABSTRACT: –

Dermatitis among humans has become a common disease, with millions suffering from various skin ailments. Often, these diseases have subtle dangers that not only lead to insecurity and depression but also lead to increased risk of skin cancer. Medical professionals and advanced instruments are needed to diagnose these skin diseases due to the lack of a feasible solution in the images of skin diseases. The proposed framework incorporates in-depth learning strategies such as CNN architecture and three predefined models called Alex Net, ResNet, InceptionV3. Dataset for seven diseases pictures has been taken to classify skin diseases. Includes diseases such as Melanoma, Nevus, Seborrheic Keratosis etc. The database was expanded by adding cut-and-burn images, classified as skin diseases by most existing systems. The use of Deep Learning algorithms has reduced the need for human activity, such as extracting features and rebuilding data for segmentation purposes.

SYSTEM:-

• Data collection module: this module will collect data in the form of images of skin diseases. The data can be collected from publicly available datasets or by taking pictures at medical institutions.
• data preprocessing module: This module will preprocess the collected data by resizing the images to a standard size, converting them to grayscale, and normalizing the pixel values.
• feature extraction module :This module will extract relevant features from the preprocessed images. It can use techniques such as convolutional neural networks (CNNs) to extract features from the images.
• Model Training Module: This module will train a machine learning model using the extracted features. It can use techniques such as support vector machines (SVMs), random forests, or neural networks.
• Model Evaluation Module:This module will evaluate the performance of the trained model by testing it on a separate set of images that were not used during training. It can use metrics such as accuracy, precision, recall, and f1 score to evaluate the model. Performance
• user interface module: This module will provide a user-friendly interface for users to input images of skin diseases and receive a classification result. It can be in the form of a web application or mobile application deployment module. This module will deploy the trained model on a server or cloud platform, making it accessible to users via the user interface module.

The skin disease classification system using machine learning can classify skin diseases into categories such as acne, eczema, psoriasis, and melanoma by providing a quick and accurate diagnosis. The system can assist medical professionals in making informed decisions about patient care.

PROPOSED SYSTEM:-

our projected system deals with classifying skin disorder once a picture of infected skin disorder is given as inputfor this we tend to chiefly centered on 2 elements image process associate degreed transfer learning the remainder of this paper is organized as follows we tend to mentioned the present system and connected works introduces the knowledgeset image process and transfer learning we tend to investigate the performance of the cnns exploitation totally completely different coaching and validation knowledge settings and analyze the results obtained through different analysis matrices conclusions and future works of this paper square measure given in section our projected system deals with classifying skin disorder once a picture of infected skin disorder is given as inputfor this we tend to chiefly centered on 2 elements image process and transfer learning the remainder of this paper is organized as follows we tend to discuss the present system and connected works introduces the knowledgeset image process and transfer learning we tend to investigate the performance of the cnns exploitation different coaching and validation data settings and analyze the results obtained through different analysis matrices conclusions and future works of this paper square measure given the projected program aims at machine-controlled computer-based medical specialty to cut back grave risks this has beyond question been a difficult task thanks to the nice sort of skin looksour projected program is to blame for classifying skin disorder once an image of an infected skin disorder is provided as a part of this that specialize in the 2 elements of image process and reading the remainder of the paper is organized as a discussion section on the present program and connected activities we tend to investigate the performance of cnn exploitation different coaching and validation data settings and analyze the results obtained with different conclusions .

MODULES:-

• data collection module: This module will collect a dataset of images of skin diseases. The dataset can be obtained from publicly available sources, medical institutions, or by taking pictures of patients’ skin.
• Data preprocessing Module:The skin data preprocessing module will preprocess the images by resizing them to a standard size, converting them to grayscale or RGB, and normalizing the pixel values. It may also involve data augmentation techniques to increase the size of the dataset feature extraction module.:This module will extract relevant features from the preprocessed images using techniques such as convolutional neural networks (CNNs). The features can be extracted from the entire image or specific regions of interest.
• Model selection module :This will select a suitable machine learning model for the classification task. Popular models include support vector machines, SVMS decision trees, random forests, and neural networks.
• Model Training Module : This module will train the selected machine learning model using the extracted features and a labeled dataset. The training process may involve hyperparameter tuning to optimize the models. Performance
• Model Evaluation Module:This module will evaluate the performance of the trained model using metrics such as accuracy, precision, recall, and f1 score. The evaluation may involve cross-validation or a separate test dataset
• deployment module: This module will deploy the trained model on a server or cloud platform, making it accessible to users through a web application or mobile application
• user interface module. This module will provide a user-friendly interface for users to input images of skin diseases and receive a classification result. The interface can also display information about the disease, its symptoms, and recommended treatments.

The skin disease classification system using machine learning can assist medical professionals in diagnosing skin diseases accurately and efficiently. It can also be used to raise awareness about skin diseases and promote preventive measures.

APPLICATION:-

• user-friendly interface :The application should have an intuitive and easy-to-use interface that allows users to upload images of skin diseases and receive a classification result.
• Image preprocessing: The application should preprocess the images by resizing them to a standard size, converting them to grayscale or RGB, and normalizing the pixel values.
• Classification algorithm: The application should use a machine learning algorithm such as convolutional neural networks (CNNs) to extract features from the images and classify them into different skin diseases accuracy and confidence scores: The application should provide the accuracy and confidence scores for each classification result to help users understand the reliability of the diagnosis
• disease information: The application should provide information about each skin disease, including its symptoms, causes, and treatments. This can be displayed alongside the classification result to help users understand the diagnosis.
• secure data handling :The application should handle user data securely, ensuring that images and any other personal information are kept confidential and not shared with third parties.
• accessibility The application should be accessible on multiple devices, such as smartphones, tablets, and desktop computers, to make it easy for users to use it from anywhere.
• updates and improvements: The application should be updated regularly to incorporate new skin diseases. improve the accuracy of the classification algorithm and enhance the user experience.

The skin disease classification application can help individuals, medical professionals, and organizations identify skin diseases accurately and efficiently by providing reliable and timely diagnoses. The application can contribute to better healthcare outcomes and promote skin health awareness.

HARDWARE AND SOFTWARE REQUIREMENTS:-

HARDWARE:-

• Linux: GNOME or KDE desktop GNU C Library (glibc) 2.15 or later, 2 GB RAM minimum,
• 4 GB RAM recommended, 1280 x 800 minimum screen resolution.
• Windows: Microsoft R Windows R 8/7/Vista (32 or 64-bit) 2 GB RAM minimum, 4 GB RAM
• recommended, 1280 x 800 minimum screen resolution, Intel R processor with support for Intel R
• VT-x, Intel R EM64T (Intel R 64) Execute Disable (XD) Bit functionality

SOFTWARE:-

• Windows Operating System.
• MySQL
• Python
• Flask
• Anaconda ,Jupyter, Spyder