Skin Disease Detection using Image Processing
Bedsores Image Dataset is implemented as input. The dataset is taken from dataset repository. The input dataset is in the format .png, .jpg. we can extract the features from pre-processed image and then we can implement the deep learning algorithm such as Convolutional Neural Network For classify the disease and lastly we can predict or classify the input image is affected or not by using classification algorithm.
Classification on Bedsores/ Skin Disease Classification using Deep Learning:-
1. Input: The dataset Bedsores image dataset is implemented as input. The dataset is taken from dataset repository. The input dataset is in the format .png, .jpg.
2. Pre-processing: The collected decrypted images are subjected to pre-processing. In the Pre- processing step, we can implement
。 Resize the images
。 Convert the gray scale images
3. Feature Extraction: In this step, we can extract the features from pre-processed image.
。 Mean standard deviation
。 Discrete Wavelet Transform (DWT) 。 Discrete Cosine Transform (DCT) 4. Image Splitting: In this step, we can split the extracted features such as
- Test image is used for prediction
- Train image is used for evaluation
5. Classification: In this step, we can implement the deep learning algorithm such as
- Convolutional Neural Network For classify the disease.
- YOLO v8
region
6. Prediction/Output:—
For detecting the affected
- In this step, we can predict or classify the input image is affected or not by using classification algorithm.
- Then, the system can predict the stage of the disease (stage 1 – 4).
- Then, the system can detect the affected region from input image by using YOLO.
7. Performance Estimation: In this step, we can analyse some performance metrics such as,
- Accuracy
- Loss/Error rate
- Comparison graph o Execution time
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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