A NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE
Malek M. AL-NAWASHI
Nawashi@bau.edu.joAl-Balqa Applied University (Jordan)
https://orcid.org/0000-0001-5641-4892
Obaida M. AL-HAZAIMEH
a:1:{s:5:"en_US";s:27:"Al-Balqa Applied University";} (Jordan)
https://orcid.org/0000-0002-5231-8155
Mutaz Kh. KHAZAALEH
Al-Balqa Applied University (Jordan)
https://orcid.org/0000-0002-2071-7020
Abstract
The leading cause of cancer-related mortality is breast cancer. Breast cancer detection at an early stage is crucial. Data on breast cancer can be diagnosed using a number of different Machine learning approaches. Automated breast cancer diagnosis using a Machine Learning model is introduced in this research. Features were selected using Convolutional Neural Networks (CNNs) as a classifier model, and noise was removed using Contrast Limited Adaptive Histogram Equalization (CLAHE). On top of that, the research compares five algorithms: Random Forest, SVM, KNN, Naïve Bayes classifier, and Logistic Regression. An extensive dataset of 3002 combined images was used to test the system. The dataset included information from 1400 individuals who underwent digital mammography between 2007 and 2015. Accuracy and precision are the metrics by which the system's performance is evaluated. Due to its low computing power requirements and excellent accuracy, our suggested model is shown to be quite efficient in the simulation results.
Keywords:
machine learning, breast cancer, CNN, image processing, healthcareReferences
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Authors
Malek M. AL-NAWASHINawashi@bau.edu.jo
Al-Balqa Applied University Jordan
https://orcid.org/0000-0001-5641-4892
Authors
Obaida M. AL-HAZAIMEHa:1:{s:5:"en_US";s:27:"Al-Balqa Applied University";} Jordan
https://orcid.org/0000-0002-5231-8155
Authors
Mutaz Kh. KHAZAALEHAl-Balqa Applied University Jordan
https://orcid.org/0000-0002-2071-7020
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