# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
###### Abstract  
In the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram screening, conducted by healthcare professionals, is often exhausting, costly, and prone to errors. To address these issues, this research proposes an end-to-end Computer-Aided Diagnosis (CAD) system utilizing the 'You Only Look Once' (YOLO) architecture. The proposed framework begins by enhancing digital mammograms using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Then, features are extracted using the proposed CNN, leveraging multiscale parallel feature extraction capabilities while incorporating DenseNet and InceptionNet architectures. To combat the 'dead neuron' problem, the CNN architecture utilizes the 'Flatten Threshold Swish' (FTS) activation function. Additionally, the YOLO loss function has been enhanced to effectively handle lesion scale variation in mammograms. The proposed framework was thoroughly tested on two publicly available benchmarks: INbreast and CBIS-DDSM. It achieved an accuracy of 98.72% for breast cancer classification on the INbreast dataset and a mean Average Precision (mAP) of 91.15% for breast cancer detection on the CBIS-DDSM. The proposed CNN architecture utilized only 11.33 million parameters for training. These results highlight the proposed framework's ability to revolutionize vision-based breast cancer diagnosis.  
breast cancer detection; breast cancer classification; mammogram screening; Contrast Limited Adaptive Histogram Equalization (CLAHE); You Only Look Once (YOLO); DenseNet; InceptionNet; Flatten Threshold Swish (FTS); INbreast; CBIS-DDSM +
Footnote †: journal: Medical Image Processing  
10 March 2024 16 March 20  
In recent years, numerous mammogram-based breast cancer diagnosis systems have been proposed [12; 14; 15; 16; 17; 18; 19]. However, there are still challenges that need addressing to enhance the performance of BC detection systems. These challenges include variations in the illumination, lesion scale, lesion class, and dynamic breast sizes in mammograms. During the early stages of the disease, lesions appear smaller in mammogram images. As the disease progresses, the lesions occupy a larger area, leading to variations in lesion size at different stages, even if the lesion type remains the same. The 'You Only Look Once' (YOLO) detection technique, a single-stage detection method, has been widely used for BCD in recent years [5; 11; 19; 20; 21; 22]. However, its loss function is not scale-invariant and is biased towards smaller lesions [23]. Additionally, traditional activation functions (ReLU, Leaky ReLU, etc.) used in their backbone architectures often lead to dead neurons and gradient vanishing issues. Addressing these challenges is crucial to enhancing the diagnostic process's efficiency and providing more accurate, efficient, and reliable diagnoses. This could potentially lead to earlier detection and improved treatment outcomes for breast cancer patients.