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September 5, 2025Open Access

Breast Tumor Classification Using Machine Learning (KNN) and Deep Learning (CNN)

Key Points

KNN and CNN are applied to classify breast tumors into benign and malignant categories.
The study evaluates both models on ultrasound images, achieving high performance metrics like accuracy and sensitivity.
KNN uses handcrafted features to establish a baseline, while CNN learns complex features automatically from images.
The findings highlight AI's potential in improving breast cancer diagnosis speed and consistency.

Abstract

Breast cancer is one of the most common cancers affecting women worldwide, and early detection is vital for improving patient outcomes. Ultrasound imaging is frequently used as a diagnostic tool because it is non-invasive, cost-effective, and safer compared to modalities such as mammography, particularly for younger patients with dense breast tissue. However, interpretation of ultrasound images relies heavily on radiologist expertise, which can be subjective and time-consuming. Artificial intelligence offers promising solutions to address these challenges by providing automated, reliable, and efficient tumor classification. This study focuses on the classification of breast tumors in ultrasound images into benign and malignant categories using two approaches: K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN). KNN, a classical machine learning algorithm, is applied on handcrafted texture features extracted from Regions of Interest (ROIs) using the Gray-Level Co-occurrence Matrix (GLCM). Features such as contrast, correlation, energy, and homogeneity capture textural variations within the tumor region. CNN, on the other hand, is a deep learning model that learns discriminative spatial and structural patterns directly from the ultrasound images, reducing the need for manual feature engineering. Both models are evaluated on breast ultrasound images with corresponding masks that precisely define the tumor boundaries. Performance is assessed using widely accepted metrics such as accuracy, sensitivity, specificity, and F1-score. KNN provides a strong baseline with interpretable results, while CNN demonstrates the ability to automatically learn complex features that may be difficult to handcraft. The work emphasizes the potential of combining machine learning and deep learning approaches to develop robust computer-aided diagnosis systems based on ultrasound imaging. Such systems can assist clinicians in achieving faster, more consistent, and more accurate breast cancer diagnosis, ultimately contributing to better patient care

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