Android based nodular application using CNN algorithm

Melanoma is a type of cancer that begins in melanocytes (cells that make the pigment melanin). Melanoma is much less common than some other types of skin cancers. But melanoma is more dangerous because it?s much more likely to spread to other parts of the body if not caught and treate

Project Title

Android based nodular application using CNN algorithm

Project Area of Specialization

Artificial Intelligence

Project Summary

Melanoma is a type of cancer that begins in melanocytes (cells that make the pigment melanin). Melanoma is much less common than some other types of skin cancers. But melanoma is more dangerous because it’s much more likely to spread to other parts of the body if not caught and treated early. Through this app we will detect the second most common type of melanoma that is Nodular melanoma through image processing.

Project Objectives

Nodular melanoma is a type of skin cancer. It’s a dangerous form of melanoma that grows quickly. A Nodular melanoma can look like a mole, bug bite, or pimple. Often, it looks like a round black bump. But it can be other colors. Currently the diagnose of nodular melanoma is done through biopsy which is quite expensive and also time consuming procedure. Therefore, to overcome this issue we will be developing an android application through which we can detect Nodular melanoma through the image processing by using Convolutional Neural Networks algorithm (CNN).

Project Implementation Method

• Development of Nodular Melonoma Detection mobile applcation using Android Studio, Python and MongoDB.
• Assortment of Datasets from enlightening sites like Kaggle and Google Datasets.
• Training of model with datasets by utilizing Convolutional Neural Networks (CNN) to recognize non-melanoma and Nodular Melanoma.
• Dployment of prepaed neural network model in a mobile application.
• Installing Amazon APIs in a trained model.
• Testing and verification of model in a mobile application.
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Benefits of the Project

• The venture will have the option to proficiently recognize shallow spreading melanoma.

• Full body photographic survey for early detection of the disease.

• Recommendations of safety measures and home remedies for patients.

• Detection of stages of the Nodular melanoma.

• Low cost and no need of overwhelming apparatus.

• Early detection of the illness.

• Ease of access for both patients/users and medical experts.

Technical Details of Final Deliverable

The last specialized deliverable of the accompanying task expresses; the portable application will be totally equipped for identifying nodular melanoma with a satisfactory rate. The last specialized deliverable report additionally states to propose home solutions for the clients if nodular melanoma isn't recognized. The accompanying report expresses another noteworthy point that the application will comprise of an element that will have the option to think about the sign of the infection from it's past check and furthermore give suggestions to keep a legitimate check towards it. Interview of clinical specialists and oncologists is an indispensable element that is accessible in the application and is a significant hypothesize referenced in the specialized expectations report of this specific endeavor.

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Education , Medical , Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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