Breast Cancer Detection using Machine Learning
Breast cancer detection using Machine Learning (ML) contains certain possibilities for early diagnosis and enhanced patient solutions. Specialized service on all research writing for Breast cancer detection using Machine Learning are offered by our team. We work with confidence and expertise PhD professionals so the success rate is always higher. By merging with various tools and techniques we achieve the desired result for the problem that we stated.
The following is the processing steps which we include in designing a project for this necessity:
- Define Objective:
- We interpret whether we are working on a binary classification (Benign vs. Malignant) and multi-class classification when analyzing various stages and kinds of breast cancer.
- Data Collection:
- Existing Datasets: The UCI ML database has the Wisconsin Breast Cancer Dataset that we broadly utilized for starting practical experiments.
- Medical Imaging Data: Datasets like the Digital Database for Screening Mammography (DDSM) serve us when we focus to predict cancer from mammogram images.
- Pre-processing the Data:
- Cleaning: We manage the lost values by removing the outliers and other discontinuities.
- Normalization/Standardization: By measuring features to ensure they are relevant we normalize the data.
- Feature Engineering: For structured data we obtain fresh features when it required. We examine the pre-processing steps such as denoising, resizing and augmentation to raise data variety.
- Exploratory Data Analysis (EDA):
- We visualize the dispersion of features, verify correlations and analyze any instability in aiming classes.
- By understanding from the EDA method we make the framework selection and instruction process.
- Model Choosing & Training:
- Classic ML for Structured Data: Approaches such as Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting Machines which we employ.
- Deep Learning for Imaging Data: For image-based tasks we implement CNNs that are highly efficient. The VGG16, ResNet and Inception are some pre-trained models we use for adjusting mammogram images in predictions.
- Training Strategy: We split the data into training, evaluation and validation sets and when the data is scarce we observe methods like cross-validation.
- Evaluation:
- Metrics: For the susceptible format of cancer forecasting we prefer some metrics like precision, recall, F1-score and ROC-AUC rather than accuracy. Based on the real-world suggestions of false positives vs. false negatives, we adapt the model’s decision threshold.
- Interpretability: It is essential to analyze how the framework makes decisions particularly in healthcare. To understand feature and area necessity we incorporate techniques such as SHAP, LIME, and Grad-CAM (for CNNs).
- Deployment:
- Web & Mobile Application: We apply our model in a user-friendly domain where healthcare experts can input data and images to get detections.
- Continuous Learning: For better accuracy we consistently improve our model with the latest data.
Project Extensions:
- Integration with Patient History: For a more integral diagnosis technique we combine the framework with patient clinical histories.
- Tumor Segmentation: To divide and highlight tumor areas we develop a model for imaging data after classification.
- Time-Series Analysis: When we get a series of medical data we monitor the progression and regression of tumors over duration.
Limitations:
- Data Security: Clinical data is vulnerable so, we make sure that entire data is hidden and that security rules are respected strictly.
- Imbalance Data: We employ methods such as SMOTE, ADASYN and data augmentation to overcome the issue of the number of positive cancer cases is much lesser than negative cases.
- Medical Validation: Our model’s detections required to clinically test by experts before the real-world deployment.
Interaction with clinical experts is essential in this project. Their observations help us to ensure that the created model attains the medical regulations and honestly serves in early and accurate breast cancer prediction.
Massive accumulated resources are there to solve the research enquires that scholars come up with. Inclusive right from beginning to end of your research work are accommodated. Online guidance are given globally for all types of ML projects. The standard of our work will be reflected at the end result where all readers will be struck in awe with our presentation method.
Breast Cancer Detection using Machine Learning Thesis Ideas
Thesis ideas from our ML experts are shared to scholars on the basis of one’s interest. Unique and innovative thesis topics are suggested, we double check the work by numerous editing and multiple revisions so as to avoid errors. The data that we have collected for breast cancer detection will be shared to scholars while detailed explanation will be given.
- Breast cancer detection based on thermographic images using machine learning and deep learning algorithms
Keywords:
Deep learning, breast cancer; thermal imaging; machine learning
We locate the tumours by the application of ML and we use the concept “binary grouping”. In this paper Computer-aided Diagnosis (CAD) method can be used to identify and diagnose the patients into 3 classes under the management of a database. Convolution networks, SVM, Random Forest are the three effective classifiers that we analyse for classification stage. In addition, we investigate the impact of mammography pictures were pre-processed and allow for higher success rate in categorization.
- Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning
Keywords:
Ensemble learning; stacking; classification; optimization; prediction
According to National Cancer Institute (NCI), if breast cancer disease is detected earlier breast cancer mortality can be decreased. The novelty of our work is to develop optimized stacking ensemble learning (OSEL) model to early prediction. The implementation analyses reveal the unique approach to categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). This study helps healthcare professionals find breast cancer and prevent it.
3. Comparative analysis of breast cancer detection using machine learning and biosensors
Keywords:
Breast cancer Detection, Biosensors
In this paper multiple algorithms based on ML approach and biosensors for early breast cancer detection have been used. we have compare and analysed various ML algorithms such as fuzzy extreme learning machine – radial basis function (ELM-RBF), SVR, RVM, naive Bayes, K-NN, decision tree , ANN, back-propagation neural network (BPNN), and random forest. Further we used biosensors to identify the presence of a biological analyte.
- Breast cancer detection using machine learning approaches: a comparative study
Keywords:
Deep learning, Classification algorithms, Breast cancer diagnosis
We use eight classification methods to predict breast cancer under exploration. A confidential dataset has been improved by applying five different feature selection methods to collect only weighted features and to remove others. Based on our work three classifiers, MLP, SVM and stack are competing with each other and to attain high accuracy. SVM is the best classifier and which performs better than even stack classier.
- Breast Cancer Detection and Classification: A Comparative Analysis Using Machine Learning Algorithms
Keywords:
Breast cancer classification, early diagnosis, Disease prediction, medical data mining.
This paper aims to perform a comparison among ML and DL methods for breast cancer detection and diagnosis. The most popular supervised ML techniques namely SVM, DT, LR, RF and KNN and a DL technique were used for classification. The Breast Cancer Wisconsin dataset can be used as a training set to evaluate and analyse the accuracy, precision, recall, specificity, F1 score etc… Random forest performs high accuracy and F1score.
- A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection
Keywords:
Breast cancer, breast histopathology image, transfer learning.
We use a hybrid dependable breast cancer detection method which fuses the power of DL using pre-trained ResNet50V2 and ensemble-based ML methods. The combination of DL enables to learn and extract hidden patterns while ML contributes interpretability and generalization capacity. We also conducted breast histopathology image-based publicly available Invasive Ductal Carcinoma (IDC) dataset of different sizes. We use Light boosting classifier (LGB) as best suited ML model.
- Breast cancer, breast histopathology image, deep learning, machine learning, transfer learning.
Keywords:
Healthcare system, cancer diagnoses
ML is widely used in breast cancer (BC) classification due to its critical feature detection from complex datasets. In this paper we propose an automatic detection of BC diagnosis and prognosis using ensemble of classifiers. We overviewed ML algorithm including ANN for automatic BC diagnosis and prognosis detection. We also study various balanced class weight on prognosis dataset and compare it with others.
8. A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning
Keywords:
Mammography images, classification performance, pre-processing methods, GLCM, GLRLM
We use CAD systems to enhance the quality of the image in mammography Images and to identify suspicious area. At first label the information and next median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) are used to increase the visibility of the image and then the suspicious regions are extracted from the mammograms using the k-means clustering technique and at last the datasets classified as normal/abnormal. CLAHE algorithm is used alone as a pre-processing method.
9. Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
Keywords:
Computer- aided detection (CAD), support vector machine (SVM), K-nearest neighbor (KNN)
This paper involves two parts, the first part involves the overview of different image modalities, and it performs preprocessing including data noise, eliminating missing values and transformation and the second part examined different ML techniques used to estimate breast cancer recurrence rates. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity.
10. Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection
Keywords:
Inverse scattering, breast phantoms, permittivity, strong dielectric scatters, born iterative method
In this paper we combine the Born iterative method with convolutional neural networks to solve the ill-framed inverse problem evolved in microwave imaging formulation in breast cancer detection. We accurately recover the permittivity of breast phantoms and several tests were carried out circular image configuration to evaluate the performance of the proposed scheme. The application of CNN allows clinicians to decrease reconstruction time.
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