Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

In the fast-growing field of medicine and its dynamic demand in research, a study that proves significant improvement to healthcare seems imperative especially when it is on cancer research. This research paved way to such significant findings by the inclusion of feature selection as one of its major components. Feature selection has become a vital task to apply data mining algorithms effectively in the real-world problems for classification. Feature selection has been the focus of interest for quite some time and much completed work related to it. Although much research conducted on the field, a study that proved a nearly perfect accuracy seems limited; hence, more scientifically driven results should be produced. Using various research on feature selection as basis for the choices in this study, the method was product of careful selection and planning. Specifically, this study used feature selection for improving classification accuracy on cancerous dataset. This study proposed Artificial Neural Network (ANN) for cancer classification with feature selection on colon cancer dataset. The study used best first search method in weka tools for feature selection. Through the process, a promising result has been achieved. The result of the experiment achieved 98.4 % accuracy for cancer classification after feature selection by using proposed algorithm. The result displayed that feature selection improved the classification accuracy based on the experiment conducted on the colon cancer dataset. The result of this experiment was comparable with the other studies on colon cancer research. It  showed another significant improvement and can be considered promising for more future applications

Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images

Segmentation of the sigmoid colon is a crucial aspect of treating diverticulitis. It enables accurate identification and localisation of inflammation, which in turn helps healthcare professionals make informed decisions about the most appropriate treatment options. This research presents a novel deep learning architecture for segmenting the sigmoid colon from Computed Tomography (CT) images using a modified 3D U-Net architecture. Several variations of the 3D U-Net model with modified hyper-parameters were examined in this study. Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance. The networks were trained using manually annotated sigmoid colon. A five-fold cross-validation procedure was used on a test dataset to evaluate the network's performance. As indicated by the maximum Dice similarity coefficient (DSC) of 56.92+/-1.42%, the application of PyP and csSE techniques improves segmentation precision. We explored ensemble methods including averaging, weighted averaging, majority voting, and max ensemble. The results show that average and majority voting approaches with a threshold value of 0.5 and consistent weight distribution among the top three models produced comparable and optimal results with DSC of 88.11+/-3.52%. The results indicate that the application of a modified 3D U-Net architecture is effective for segmenting the sigmoid colon in Computed Tomography (CT) images. In addition, the study highlights the potential benefits of integrating ensemble methods to improve segmentation precision.Comment: 8 Pages, 6 figures, Accepted at IEEE DICTA 202

An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers