Kok Swee Sim
Multimedia University
63 Papers
74 Citations
Kok Swee Sim is an academic researcher from Multimedia University. The author has contributed to research in topics: Histogram equalization & Adaptive histogram equalization. The author has an hindex of 6, co-authored 63 publications.
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Papers
Convolutional neural network improvement for breast cancer classification
TL;DR: The algorithm called Convolutional Neural Network Improvement for Breast Cancer Classification (CNNI-BCC) is presented to assist medical experts in breast cancer diagnosis in timely manner using a convolutional neural network that improves the breast cancer lesion classification.
400
Breast cancer detection using convolutional neural networks for mammogram imaging system
Y. J. Tan,Kok Swee Sim,F. F. Ting +2 more
- 01 Nov 2017
TL;DR: BCDCNN method with Mammogram Classification Using Convolutional Neural Networks (MCCNN) has improved the accuracy toward classification on the mammogram images and the results show that the proposed method has higher accuracy than other existing methods.
130
Contrast enhancement dynamic histogram equalization for medical image processing application
TL;DR: A contrast‐enhancement dynamic histogram‐equalization algorithm method that generates better output image by preserving the input mean brightness without introducing the unfavorable side effects of checkerboard effect, artefacts, and washed‐out appearance is introduced.
40
Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network
TL;DR: In this article, a Convolution Attention Memory Neural Network (CAMNN) model was proposed to classify participants' EEG signals as showing either attentive or inattentive behaviors.
A Novel Approach to Objectively Quantify the Subjective Perception of Pain Through Electroencephalogram Signal Analysis
TL;DR: This study integrated signal processing techniques and machine learning principles to learn brain signals associated with pain and classify them into one of four pain intensities and found that the signal processing revealed a direct correlation between Alpha frequency band power and the pain intensity, and the classifier could achieve an accuracy of 94.83%.