Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach

Feature-based detection of breast cancer using convolutional neural network and feature engineering

Breast cancer (BC) is a prominent cause of female mortality on a global scale. Recently, there has been growing interest in utilizing blood and tissue-based biomarkers to detect and diagnose BC, as this method offers a non-invasive approach. To improve the classification and prediction of BC using large biomarker datasets, several machine-learning techniques have been proposed. In this paper, we present a multi-stage approach that consists of computing new features and then sorting them into an input image for the ResNet50 neural network. The method involves transforming the original values into normalized values based on their membership in the Gaussian distribution of healthy and BC samples of each feature. To test the effectiveness of our proposed approach, we employed the Coimbra and Wisconsin datasets. The results demonstrate efficient performance improvement, with an accuracy of 100% and 100% using the Coimbra and Wisconsin datasets, respectively. Furthermore, the comparison with existing literature validates the reliability and effectiveness of our methodology, where the normalized value can reduce the misclassified samples of ML techniques because of its generality.

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

Breast cancer is one of the most often diagnosed cancers in women, and identifying breast cancer histological images is an essential challenge in automated pathology analysis. According to research, the global BrC is around 12% of all cancer cases. Furthermore, around 25% of women suffer from BrC. Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated in this work the viability of automatically identifying and classifying BrC. The first stage is pre-processing, which employs an Adaptive Switching Modified Decision Based Unsymmetrical Trimmed Median Filter (ASMDBUTMF) to remove high-density noise. After the image has been pre-processed, it is segmented using the Thresholding Level set approach. Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. The suggested strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure. Additionally, it aids in resolving problems pertaining to global optimization. Following the selection, the best characteristics proceed to the categorization procedure. A DL classifier called the Conditional Variation Autoencoder is used to discriminate between cancerous and benign tumors while categorizing them. Consequently, a classification accuracy of 99.4%, Precision of 99.2%, Recall of 99.1%, F- score of 99%, Specificity of 99.14%, FDR of 0.54, FNR of 0.001, FPR of 0.002, MCC of 0.98 and NPV of 0.99 were obtained using the proposed approach. Furthermore, compared to other research using the current BreakHis dataset, the results of our research are more desirable.

Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques

Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

A voting-based machine learning approach for classifying biological and clinical datasets

BACKGROUND

Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods.

RESULTS

The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure.

CONCLUSION

Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.

Application of Deep Learning System Technology in Identification of Women’s Breast Cancer

Background and Objectives: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. Materials and Methods: This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. Results: The major contribution of this research was the application of CNNs as a machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. Conclusions: This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.

A novel discrete learning-based intelligent methodology for breast cancer classification purposes

Classification is one of the most significant subfields of data mining that has been successfully applied to various applications. The literature has expended substantial effort to present more efficient and accurate classification models. Despite the diversity of the proposed models, they were all created using the same methodology, and their learning processes ignored a fundamental issue. In all existing classification model learning processes, a continuous distance-based cost function is optimized to estimate the unknown parameters. The classification problem's objective function is discrete. Consequently, applying a continuous cost function to a classification problem with a discrete objective function is illogical or inefficient. This paper proposes a novel classification methodology utilizing a discrete cost function in the learning process. To this end, one of the most popular intelligent classification models, the multilayer perceptron (MLP), is used to implement the proposed methodology. Theoretically, the classification performance of the proposed discrete learning-based MLP (DIMLP) model is not dissimilar to that of its continuous learning-based counterpart. Nevertheless, in this study, to demonstrate the efficacy of the DIMLP model, it was applied to several breast cancer classification datasets, and its classification rate was compared to that of the conventional continuous learning-based MLP model. The empirical results indicate that the proposed DIMLP model outperforms the MLP model across all datasets. The results demonstrate that the presented DIMLP classification model achieves an average classification rate of 94.70 %, a 6.95 % improvement over the classification rate of the traditional MLP model, which was 88.54 %. Therefore, the classification approach proposed in this study can be utilized as an alternative learning process in intelligent classification methods for medical decision-making and other classification applications, particularly when more accurate results are required.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.