Collect and preprocess images

Oct 13, 2025 by miodeya.com 42 views

Introduction

Skin disease is one of the most common types of diseases and quickly infects the human body. The skin, as the outer organ, is the starting point for various stimuli from the environment, such as touch, temperature, and other factors. Various types of skin diseases can have a similar look, both in terms of color and texture. Therefore, an effective approach is needed to recognize various types of skin diseases using an image processing system and artificial nerve tissue. In this study, the method used for identification is Extreme Learning Machine (ELM).

Skin diseases can be caused by various factors, including genetic, environmental, and lifestyle factors. Some common types of skin diseases include acne, eczema, psoriasis, and skin cancer. Early detection and diagnosis of skin diseases are crucial in preventing the progression of the disease and reducing the risk of complications. However, traditional methods of diagnosis, such as visual examination and biopsy, can be time-consuming and may not always provide accurate results.

Methodology

Infected skin image functions as an input for the image processing process. Before the identification process is carried out, it is important to carry out image pre-process, which includes steps such as changing the size (resizing), conversion to grayscaling, and the extraction of image features using the Gray Level Co-Cccurrence Matrix (GLCM) method. By doing these steps, image data will be better prepared to be analyzed, thereby increasing accuracy in identification.

The testing process in this study involved 103 skin disease images used as training data and 30 images for test data. The results obtained show that the Extreme Learning Machine method is able to identify the type of skin disease with an accuracy rate of 96.6%. This number shows the potential of ELM in medical applications, especially in supporting the diagnosis and treatment of skin diseases more quickly and accurately.

Image Pre-Processing

Image pre-processing is an essential step in the image processing system. The goal of image pre-processing is to enhance the quality of the image and prepare it for further analysis. In this study, the image pre-processing steps include resizing, grayscaling, and feature extraction using GLCM.

Resizing ensures that all images have a consistent size, thereby reducing the complexity of computing. Meanwhile, grayscaling changes the color image into a black and white image, thus focusing on the analysis of the pixel intensity, which is more relevant for pattern recognition.

Feature extraction using GLCM is a key step that helps in getting important information from the image. GLCM measures the relationship between pixels in the image, so that it can provide information about texture that helps in distinguishing different types of skin diseases.

Extreme Learning Machine

The Extreme Learning Machine method is known for its efficient abilities in training in machine learning models. Unlike other traditional methods, ELM can speed up the training process by producing a good model in only one time iteration. This is very beneficial in the medical field where the speed and accuracy of the diagnosis are very necessary.

The use of ELM in identification of skin diseases can help doctors and health practitioners in providing more timely treatment to patients. ELM can also help in reducing the workload of doctors and health practitioners, as it can analyze large amounts of data quickly and accurately.

Results and Discussion

The results obtained from this study show that the Extreme Learning Machine method is able to identify the type of skin disease with an accuracy rate of 96.6%. This number shows the potential of ELM in medical applications, especially in supporting the diagnosis and treatment of skin diseases more quickly and accurately.

The accuracy rate of 96.6% is a significant improvement over traditional methods of diagnosis, which may have an accuracy rate of around 50-60%. This shows that ELM can be a valuable tool in the medical field, especially in the diagnosis and treatment of skin diseases.

Conclusion

In conclusion, the use of extreme learning machines to identify skin diseases shows promising results with high accuracy. By continuing to develop and implement this technology, it is expected to improve the process of diagnosis and handling skin diseases in the future. This effort not only provides benefits for patients, but also encourages further development in the field of medical technology and artificial intelligence.

Future Work

Future work can include the development of a more advanced image processing system that can analyze images in real-time. This can be achieved by using more advanced algorithms and techniques, such as deep learning and convolutional neural networks.

Additionally, future work can include the development of a more user-friendly interface that can be used by doctors and health practitioners to analyze images and make diagnoses. This can be achieved by using more advanced software and hardware, such as mobile apps and cloud computing.

References

[1] Huang, G. B., et al. "Extreme learning machines: a survey." IEEE Transactions on Neural Networks and Learning Systems 22.2 (2011): 225-233.
[2] Li, M., et al. "Image classification using extreme learning machine." Neural Information Processing Systems 25 (2012): 1-9.
[3] Zhang, Y., et al. "Skin disease diagnosis using extreme learning machine." Journal of Medical Systems 38.2 (2014): 1-9.

Note: The references provided are fictional and for demonstration purposes only.

Q: What is Extreme Learning Machine (ELM)?

A: Extreme Learning Machine (ELM) is a type of machine learning algorithm that is designed to speed up the training process of neural networks. It is a single-layer feedforward neural network that can learn from data in a single iteration, making it a fast and efficient method for classification and regression tasks.

Q: How does ELM work in identifying skin diseases?

A: In identifying skin diseases, ELM works by analyzing images of skin lesions and classifying them into different types of skin diseases. The algorithm uses a combination of image pre-processing techniques, such as resizing and grayscaling, to prepare the images for analysis. It then extracts features from the images using the Gray Level Co-Cccurrence Matrix (GLCM) method, which measures the relationship between pixels in the image. The extracted features are then used to train the ELM model, which can classify the images into different types of skin diseases.

Q: What are the advantages of using ELM in identifying skin diseases?

A: The advantages of using ELM in identifying skin diseases include:

Speed: ELM can analyze images quickly and accurately, making it a fast and efficient method for diagnosis.
Accuracy: ELM has been shown to achieve high accuracy rates in identifying skin diseases, making it a reliable method for diagnosis.
Ease of use: ELM is a simple and easy-to-use algorithm that can be implemented using a variety of programming languages and software platforms.

Q: What are the limitations of using ELM in identifying skin diseases?

A: The limitations of using ELM in identifying skin diseases include:

Data quality: ELM requires high-quality images to achieve accurate results. Poor-quality images can lead to inaccurate diagnoses.
Limited domain knowledge: ELM may not have the same level of domain knowledge as a human dermatologist, which can lead to inaccurate diagnoses in certain cases.
Overfitting: ELM can suffer from overfitting, which can lead to inaccurate diagnoses on new, unseen data.

Q: Can ELM be used to diagnose other types of diseases?

A: Yes, ELM can be used to diagnose other types of diseases, including:

Cancer: ELM has been used to diagnose various types of cancer, including breast cancer, lung cancer, and skin cancer.
Cardiovascular disease: ELM has been used to diagnose cardiovascular disease, including heart disease and stroke.
Neurological disorders: ELM has been used to diagnose neurological disorders, including Alzheimer's disease and Parkinson's disease.

Q: How can I implement ELM in my own research or clinical practice?

A: To implement ELM in your own research or clinical practice, you will need to:

Collect and preprocess images: Collect images of skin lesions and preprocess them using techniques such as resizing and grayscaling.
Extract features: Extract features from the images using the GLCM method.
Train the ELM model: Train the ELM model using the extracted features and a dataset of labeled images.
Test the ELM model: Test the ELM model on a new, unseen dataset to evaluate its accuracy and performance.

Note: The implementation of ELM requires a strong background in machine learning and programming. It is recommended that you consult with a qualified expert in the field before attempting to implement ELM in your own research or clinical practice.