Breast cancer classification using machine learning techniques: a comparative study

Background: The second leading deadliest disease affecting women worldwide, after  lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed.  These approaches show  their effectiveness in data classification in many fields, especially in healthcare.      Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification.  The used dataset is  Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave  the best results with an accuracy of 98% for breast cancer classification.       Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are  performant  ( good accuracy specificity and sensitivity) compared to the  other evaluated algorithms

An Improved Model for Breast Cancer Diagnosis by Combining PCA and Logistic Regression Techniques

Abstract: Breast cancer is weighed one of the most life-threatening illnesses confronting women. It happens when the multiplication of cells in breast tissue is uncontrollable. Several studies have been performed in the healthcare field for early breast cancer diagnosis. However, traditional methods can generate incomplete or misleading outcomes. To overcome these limitations, computer-aided diagnosis (CAD) systems are extensively exploited in the healthcare domain. It is designed to improve accuracy, decrease complexity, and reduce misclassification costs. The goal of this study is to present a breast cancer CAD system based on combining the Principal Component Analysis (PCA) method for feature reduction and Logistic Regression (LR) for BC tumors classification. The experiments have been conducted on Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Original Breast Cancer (WOBC) datasets from UCI repository using different training and testing subsets. Moreover, we carried out extensive comparisons of our approach with other existing approaches. Multiple metrics like precision, F1 score, recall, accuracy, and Area Under Curve (AUC) were used in this study. Experimental results indicate that the proposed approach records a remarkable performance rate with an accuracy of 1.00 and 0.98 for WDBC and WOBC respectively and outperforms the previous works by decreasing the number of features, improving the data quality, and reducing the response time.16 página

Algorithmes Evolutionnaires pour la Segmentation d'Images basée sur les Automates Cellulaires

L'objectif de notre travail vise à s'inspirer des phénomènes naturels a�n d'utiliser leurs puissances dans la résolution des problèmes di�ciles. Ce travail consiste à explorer le domaine des algorithmes génétiques et des systèmes complexes (SCs). Dans cette thèse, nous nous sommes intéressés à l'exploitation du phénomène de l'émergence dans le problème de détection de contours des images en couleurs. Nous utilisons les Automates Cellulaires (ACs) qui sont un outil puissant de modélisation basée sur l'émergence. Nous avons choisis les Algorithmes Génétiques (AGs) pour déterminer les meilleures règles données pour la détection de contours des images en couleurs. Nous visons une implémentation de cette approche pour valider et détecter les meilleures règles (paquets). Nous avons déterminé les rôles de chaque règle surtout dans la détection de contours

Evolutionary Approach Based on Active Edges Detection for Images Segmentation

There are many methods for segmentation which vary strongly in their approach to the problem of image segmentation. In this paper, We specified the study in a particular segmentation method of radiological images based on the active edges detection. The optimize solutions was chosen as the genetic algorithm optimization method, and to compare this formalism with other existing methods, we chose a greedy algorithm is criterion for its timeliness. we propose a method of genetic active edge detection in images gray level. In fact, for the convergence of the edge to the object edges, we use the classic and the greedy method. Indeed, the proposed method is based on the active edges optimization using the genetic algorithms process to minimize a sum various energies, in order to evolve a population of snakes to an individual who has the minimum energy