2020/02/20 by Ke Quan, Quan, Ke
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Medicine · #AI in cancer detection #Algorithm #Artificial intelligence #Artificial neural network #Breast cancer #Cancer #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Feature selection #Gene expression and cancer classification #Genetic algorithm #Genetic programming #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine learning #Medicine #Neural and Evolutionary Computing (cs.NE) #Pattern recognition (psychology) #Process (computing) #Selection (genetic algorithm) #cs.CV #cs.LG #cs.NE #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.08729
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/02/20 · openalex publication_date 2020/02/20 · arxiv updated 2020/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Diagnosis of breast cancer has been well studied in the past. Multiple linear programming models have been devised to approximate the relationship between cell features and tumour malignancy. However, these models are less capable in handling non-linear correlations. Neural networks instead are powerful in processing complex non-linear correlations. It is thus certainly beneficial to approach this cancer diagnosis problem with a model based on neural network. Particularly, introducing bias to neural network training process is deemed as an important means to increase training efficiency. Out of a number of popular proposed methods for introducing artificial bias, Bimodal Distribution Removal (BDR) presents ideal efficiency improvement results and fair simplicity in implementation. However, this paper examines the effectiveness of BDR against the target cancer diagnosis classification problem and shows that BDR process in fact negatively impacts classification performance. In addition, this paper also explores genetic algorithm as an efficient tool for feature selection and produced significantly better results comparing to baseline model that without any feature selection in place