Implementation of Deep Learning Approach for Detection and Prediction of Breast Cancer Test phase, srs, design phase and source code final deliverable

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Implementation of Deep Learning Approach for Detection and Prediction of Breast Cancer Test phase, srs, design phase and source code final deliverable

Project Domain / Category

Artificial Intelligence: Deep learning

Abstract/Introduction

Early detection and prediction of medical problems can save the life of human beings. It is complicated and expensive to detect on manual basis detection regularly by domain experts and it will not give accurate predictions. Artificial intelligence techniques are the better approach to automatic cancer disease detection and diagnosis with highly accurate results. Breast cancer is an important factor affecting women’s health. The program will be implemented to detect and predict breast cancer diseases by using deep learning methods such as the classification of normal, benign, and malignant tissues. In this system, it will be considered requirements that utilize breast cancer images repository datasets for experimentation.

Functional Requirements:

1. There are seven major tasks you will typically perform when developing a system. Tasks (2-7) should be implemented while developing the system.
   1. Task 1: Select the image datasets of breast cancer disease.
   2. Task 2: Image Data Analysis and Pte-processing iii. Task 3: Feature Extraction iv. Task 4: Detection
   3. Task 5: Build system vi. Task 6: Test System vii. Task 7: Tune System
2. The program should have a knowledge-based system according to select image data.
3. The program should have the deep learning approach to execute model for detection.
4. The program should evaluate the performance and update knowledge based on the requirement.

Note: Skype sessions must be attended to communicate with the supervisor about deep learning methods and dataset’s discussion otherwise project will not be accepted.

Tools/language:   Python programming language,

Advanced libraries: Keras, OpenCV, NumPy, Pillow, SciPy, and TensorFlow etc

DataSet: Datasets details will be provided by Skype sessions.

Prerequisite: For Deep Learning Concepts, students will have to cover a short course relevant to the mentioned concepts besides SRS and design initial documentation. It will also be provided course links during Skype sessions.

Supervisor:

Name: Dr. Saima Munawar