ENS5251 Digital Signal Processing : Development of The Algorithm

ENS5251 Digital Signal Processing : Development of The Algorithm

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ENS5251 Digital Signal Processing

Task:

In the United States in 2015, for example, approximately 350,000 of melanoma cases were identified, with around 60,000 deaths. Skin cancer is known as an abnormal condition in which skin cells multiply rapidly due to DNA mutation or generic defects. In medical science, the disease is classified as melanoma and non- melanoma, of which melanoma is one of the deadliest diseases with 98% incurable worldwide.

Although the mortality is considerably high, there is a high chance for a cure if the disease is detected early. In this project, students will investigate and develop an image processing technique to detect and classify the skin conditions, classified as Benign or Malignant. Students are encouraged to apply DSP techniques for image processing to improve the classification performance.

Group Information

The project will be tackled in group of 3 students (no more, no less). Group members must discuss together and assign each member to one of the following components:

(a) Algorithm submission

(b) Report Submission

(c) Poster Submission & Presentation

You also need to nominate one member as the team leader who will be responsible for communicating with lecturer via email member’s responsibilities by the due date.

Literature Review

As a group, conduct a thorough literature review on related works and present your findings in the written report.

Algorithm Development

Each group will develop their own classification algorithm based on its literature review. The proposed and developed algorithm must be able to classify the image as benign or malignant. Your algorithm must be robust against scale, orientation, lighting variance, and strong enough to identify the variety of skin moles.

The key idea of the unit project is to help students further developing the knowledge and skills in Digital Signal Processing, particularly in Image Processing. Applying image processing techniques to enhance the image quality will play a key role in improving the performance of developed algorithm regardless of a machine learning technique was used. Note that there are no requirements on how to proceed the development of the algorithm; students may want to:

• Remove the skin hair to improve the mole visibility.

• Study the characteristics of skin moles of both benign and malignant. e.g. shape, color, size, distribution, and so on.

• Develop and test the algorithm using the studied characteristics.

• Study on how to enhance image quality using image processing techniques and further improve the algorithm with noisier images (images with various background, blurry images, …). Your algorithm must meet the following criteria:

(a) Written in MATLAB. There may be multiple script files but should have a main script to run the algorithm, named as main.m.

(b) Prompt the user for a folder which contains a set of input images in JPEG format.

(c) Perform the image classification task on all images within the specified folder. The classification will be solely based on the input images. You must not use the filename or provide any additional information to assist the algorithm.

(d) Display the classification results on the screen by showing each image filename and its associated result.

(e) Take less than 10 seconds to execute each classification (preferably instantaneous). For example, if there are 10 image files in the folder, then your upper limit is 100 seconds.

 Report

A group must work together and write a 4-page double-column paper documenting the work that you have achieved. Report:

• Must use the given template.

• Do not amend any of the given headings.

• Should not exceed the 4-page limit.

• Include the confusion matrix (from testing set), the success rates of your training and testing, and the execution speed must be presented. Submit the report by the due date. Standard penalty for late submission will be applied according to the school policy.

ENS5251 Digital Signal Processing

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