The week's pick
Reseach Article
CNN-based Early Diagnosis of Breast Cancer with Variable Image Resolutions
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 44 |
| Year of Publication: 2024 |
| Authors: Miraj Jara, Alaa Sheta, Walaa H. Elashmawi |
10.5120/ijca2024924027
|
Miraj Jara, Alaa Sheta, Walaa H. Elashmawi . CNN-based Early Diagnosis of Breast Cancer with Variable Image Resolutions. International Journal of Computer Applications. 186, 44 ( Oct 2024), 1-8. DOI=10.5120/ijca2024924027
Abstract
Breast cancer remains one of the most common and lethal forms of cancer globally. It impacts millions of women each year across various age groups, ethnicities, and socio-economic backgrounds. Early detection is critical for improving survival rates, as it allows for timely and less aggressive treatment options. While mammography has played a significant role in early diagnosis, it is limited by variability in interpretation and the potential for false positives and negatives. Recent deep learning (DL) advancements offer promising solutions for more accurate breast cancer detection. This paper presents a convolutional neural network (CNN) architecture designed to classify breast ultrasound images, distinguishing between malignant, benign, and normal tissues. The proposed CNN model was trained and tested on the Breast Ultrasound Images dataset with various image resolutions, including 32×32, 56×56, 128×128, and 256×256 pixels. The results demonstrated that with an image resolution of 256×256, the CNN model achieved the highest accuracy of 99.87% and 83.49% in training and testing, respectively. The study emphasizes the potential of deep learning techniques in improving breast cancer detection accuracy and efficiency, ultimately leading to improved patient outcomes.
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