Breast cancer detection based on thermographic images using machine learning and deep learning algorithms

Allugunti, Viswanatha Reddy

doi:10.33545/26633582.2022.v4.i1a.68

Cited by 93 publications

(11 citation statements)

References 8 publications

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“…The breast cancer dataset from Wisconsin is utilized for comparison. (Allugunti 2022) Based on the assessment findings, the random forest approach yielded the highest accuracy (99.76%) with the lowest amount of error. Every experiment may be carried out repeatedly thanks to the use of the Anaconda Data Science Platforms.…”

Section: Et Al (2020) Containmentioning

confidence: 97%

Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models

Ayepeku

2024

SISTEMASI

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This research presents an in-depth exploration of breast cancer prediction through the application of machine learning models, specifically focusing on Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, 'Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, AdaBoost Classifier, and XGBoost Classifier. The study utilizes a comprehensive dataset comprising clinical features extracted from Kaggle. Various algorithms are employed, and a meticulous analysis of precision, recall, F1-score, and accuracy is conducted to assess model performance. Through advanced visualization techniques and statistical analysis, the research provides insights into the effectiveness of machine learning models in predicting breast cancer. The outcomes of this study aim to contribute valuable knowledge to the field of medical diagnostics, emphasizing the importance of machine learning methodologies in enhancing breast cancer prediction and classification.

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Section: Et Al (2020) Containmentioning

confidence: 97%

Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models

Ayepeku

2024

SISTEMASI

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“…Allugunti [ 19 ] suggested a computer-aided diagnosis (CAD) approach for classifying patients into three categories (non-cancerous, no cancer, and cancer) and making a diagnosis using a database. The author studied and examined three efficient classifiers for the classification stage: CNN, SVM, and random forest (RF).…”

Section: Literature Studymentioning

confidence: 99%

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

et al. 2023

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Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

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“…The results of the evaluations showed that the random forest algorithm achieved the highest level of accuracy (99.76%) with the least amount of error. The Anaconda Data Science Platforms were used to run all the experiments in a reproducible environment [26]. The authors (Allugunti 2022) proposed an approach for breast cancer that classifies the disease into its various subgroups.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Prevention Using Machine Learning

Khalid,

Mehmood,

Alabrah

et al. 2023

Diagnostics

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Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.

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Breast cancer detection based on thermographic images using machine learning and deep learning algorithms

Cited by 93 publications

References 8 publications

Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models

Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Breast Cancer Detection and Prevention Using Machine Learning

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