Reliable early breast cancer detection using artificial neural network for small data set

• Main Authors: Vijayasarveswari, V., Jusoh, M., Sabapathy, T., Raof, R. A. A., Sabira, Khatun, Iszaidy, I.
• Format: Conference or Workshop Item
• Language: English
• Published: IOP Publishing 2021
• Subjects: RC Internal medicine; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/31849/1/Reliable%20early%20breast%20cancer%20dDetection%20using%20artificial%20neural%20network.pdf; http://umpir.ump.edu.my/id/eprint/31849/; https://doi.org/10.1088/1742-6596/1755/1/012037

This paper proposes a breast cancer detection module using Artificial Neural Network for small data set. The developed system consists of hardware and software. Hardware included UWB transceiver and a pair of home- made directional sensor/antenna. The software included a Graphical User Interface (GUI) and k-fold based feed-forward back propagation Neural Network module to detect the tumor existence, size and location along with soft interface between software and hardware. Forward scattering technique is used by placing two sensors diagonally opposite sides of a breast phantom. UWB pulses are transmitted from one side of phantom and received from other side, controlled by the software interface in PC environment. Firstly feed forward backpropagation neural network (FFBNN) is developed. Then, k-fold is combined with developed FFBNN for testing purpose. Four data sets are created where contains 125, 95, 65 and 30 data samples in 1st,2nd,3rd and 4th data set respectively. Collected received signals were then fed into the NN module for training, testing and validation. The process is done for all data sets separately. The system exhibits detection efficiency of tumor existence, location (x, y, z), and size were approximately 87.72%, 87.24%, 83.93% and 80.51% for 1st, 2nd, 3rd and 4th data set respectively. The proposed module is very practical with low-cost and user friendly. The developed breast cancer detection module can be used for large data samples as well as for minimum data samples.