Breast Cancer Classification Problem Research Articles

EFFICIENT BREAST CANCER PREDICTION USING ML TECHNIQUES

During their life, among 8% of women are diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped worlds. BC is characterized by the mutation of genes, constant pain, changes in the size, color(redness), skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic, generally the most frequent classification is binary (benign cancer/malign cancer). Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this system, we present two different classifiers: Decision tree and logistic regression for breast cancer classification. We propose a comparison between the two new implementations and evaluate their accuracy using cross validation.

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.

Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem.

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer mortality in women around the world. However, it can be controlled effectively by early diagnosis, followed by effective treatment. Clinical specialists take the advantages of computer-aided diagnosis (CAD) systems to make their diagnosis as accurate as possible. Deep learning techniques, such as the convolutional neural network (CNN), due to their classification capabilities on learned feature methods and ability of working with complex images, have been widely adopted in CAD systems. The parameters of the network, including the weights of the convolution filters and the weights of the fully connected layers, play a crucial role in the classification accuracy of any CNN model. The back-propagation technique is the most frequently used approach for training the CNN. However, this technique has some disadvantages, such as getting stuck in local minima. In this study, we propose to optimize the weights of the CNN using the genetic algorithm (GA). The work consists of designing a CNN model to facilitate the classification process, training the model using three different optimizers (mini-batch gradient descent, Adam, and GA), and evaluating the model through various experiments on the BreakHis dataset. We show that the CNN model trained through the GA performs as well as the Adam optimizer with a classification accuracy of 85%.

Breast cancer Data classification Using Machine Learning Mechanisms

During their life, among 8% of women is diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness), skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic, generally the most frequent classification is binary (benign cancer/malign cancer). Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we apply five different machine learning algorithms for classifying BC data, and results are visually represented.

Optimal breast cancer classification using Gauss–Newton representation based algorithm

Abstract Breast cancer is a decisive disease worldwide. It is one of the most widely spread cancer among women. As per the survey, one out of eight women in the world are at risk of breast cancer at some point of time in her life. One of the methods to reduce breast cancer mortality rate is timely detection and effective treatment. That is why, more accurate classification of a breast cancer tumor has become a challenging problem in the medical field. Many classification techniques are proposed in the literature. Today, expert systems and machine learning techniques are being extensively used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we have proposed a novel Gauss-Newton representation based algorithm (GNRBA) for breast cancer classification. It uses the sparse representation with training sample selection. Until now, sparse representation has been successfully applied in pattern recognition only. The proposed method introduces a novel Gauss-Newton based approach to find the optimal weights for the training samples for classification. In addition, it evaluates the sparsity in a computationally efficient way as compared to the conventional l1-norm method. The effectiveness of the GNRBA is examined on the Wisconsin Breast Cancer Database (WBCD) and the Wisconsin Diagnosis Breast Cancer (WDBC) database from the UCI Machine Learning repository. Various performance measures like classification accuracy, sensitivity, specificity, confusion matrices, a statistical test and the area under the receiver operating characteristic (AUC) are reported to show the superiority of the proposed method as compared to classical models. The experimental results show that the proposed GNRBA could be a good alternative for breast cancer classification for clinical experts.

Intelligent System for Detection of Micro-Calcification in Breast Cancer

Recently; medical image mining has become one of the well-recognized research area(s) of machine learning and artificial intelligence techniques have been vastly used in various computer added diagnostic systems. Specifically; breast cancer classification problem is considered as one of the most significant problems. For instance, complex, diverse and heterogamous malignant features of micro-calcification in DICOM (Digital Communication in Medicine) images of mammography are very difficult to classify because the persistence of noise in mammogram images creates lots of confusions for doctors. In order to reduce the chances of misdiagnosis and to discernment the difference between malignant and benign lesions of micro-calcification this paper proposes a system so called “Intelligent System For Detection of Micro-Calcification in Breast Cancer” by considering all above stated problems. Overall our system comprises over three main stages. In first stage, adaptive threshold algorithm is used to reduce the noise, and canny edge detection algorithm is used to detect the edges of every macro or micro classification. In second stage, deginated as feature selection is done by using auto-crop algorithm, which crops all types of calcifications and lesions by proposed algorithm so called CFEDNN (Calcification Feature Extraction Deep Neural Networks) which is designed to avoid the manual ROIs (Region of Interest). Decision model is constructed by using DNN (Deep Neural Networks) and the best classification accuracy is measured as 95.6%.

New Deep Kernel Learning based Models for Image Classification

Deep learning system is used for solving many problems in different domains but it gives an over-fitting risk when richer representations are increased. In this paper, three different models with different deep multiple kernel learning architectures are proposed and evaluated for the breast cancer classification problem. Discrete Wavelet transform and edge histogram descriptor are used to extract the image features. For image classification purpose, support vector machine with the proposed deep multiple kernel models are used. Also, the span bound is employed for optimizing these models over the dual objective function. Furthermore, the comparison between the performance of the traditional support vector machine which uses only single kernel and the introduced models is worked out that show the efficiency of the experimental results of the proposed models.

Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem.

Generating Interpretable Fuzzy Systems for Classification Problems

En este artículo se presenta un nuevo método para generar sistemas difusos interpretables, a partir de datos experimentalesde entrada y salida, para resolver problemas de clasificación. En la partición antecedente se emplean conjuntos triangulares con interpolación de 0.5 lo cual evita la presencia de solapamientos complejos que suceden en otros métodos. Los consecuentes, tipo Singleton, son generados por la proyección de los valores modales de cada función de membresía triangular en el espacio de salida y se emplea el método de mínimos cuadrados para el ajuste de los consecuentes. El método propuesto consigue una mayor precisión que la alcanzada con los métodos actuales existentes, empleando un número reducido de reglas y parámetros y sin sacrificar la interpretabilidad del modelo difuso. El enfoque propuesto es aplicado a dos problemas clásicos de clasificación: el Wisconsin Breast Cancer (WBC) y el Iris Data Classification Problem, para mostrar las ventajas del método y comparar los resultados con los alcanzados por otros investigadores.

Feature selection and classification using flexible neural tree

The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.

Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization

The data-driven identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A binary decision-tree-based initialization of fuzzy classifiers is proposed for the selection of the relevant features and effective initial partitioning of the input domains of the fuzzy system. Fuzzy classifiers have more flexible decision boundaries than decision trees (DTs) and can therefore be more parsimonious. Hence, the decision tree initialized fuzzy classifier is reduced in an iterative scheme by means of similarity-driven rule-reduction. To improve classification performance of the reduced fuzzy system, a genetic algorithm with a multiobjective criterion searching for both redundancy and accuracy is applied. The proposed approach is studied for (i) an artificial problem, (ii) the Wisconsin Breast Cancer classification problem, and (iii) a summary of results is given for a set of well-known classification problems available from the Internet: Iris, Ionospehere, Glass, Pima, and Wine data.

Bayesian inference for hierarchical mixtures-of-experts with applications to regression and classification.

This paper studies the problems of inference and prediction in a class of models known as hierarchical mixtures-of-experts (HME). The statistical model underlying an HME is a mixture model in which both the mixture coefficients and the mixture components are generalized linear models. Bayesian inference regarding an HME's parameters is presented in the contexts of regression and classification using Markov chain Monte Carlo methods. A benefit of this Bayesian approach is the ability to obtain a sample from the posterior distribution of any functional of the parameters of the given model. In this way, more information is obtained than provided by a point estimate. The methods are illustrated on a nonlinear regression problem and on a breast cancer classification problem. The results indicate that the HME showed good prediction performance, and also gave the additional benefit of providing for the opportunity to assess the degree of certainty of the model in its predictions.