# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
### Current State-of-the-Art in BCD  
In recent years, a myriad of expert systems have been developed for the diagnosis of breast cancer using mammograms, primarily falling into two categories: (i) conventional image processing techniques and (ii) deep learning-based diagnosis [24]. Traditional image processing approaches in breast cancer detection involve steps like image enhancement, handcrafted feature extraction, and feature classification. Previously, widely recognized feature extraction methods such as Local Binary Patterns (LBP) [25], Gray Level Cooccurrences Matrix (GLCM) [25; 26], Histogram of Oriented Gradients (HOG) [26], and Scale Invariant Feature Transform (SIFT) [25; 27] have been utilized. Simultaneously, machine-learning classifiers like Support Vector Machine (SVM) [25], Naive Bayes (NB) [28], and Random Forest (RF) [25; 28] have been employed to distinguish between malignant and benign tumors in mammographic images. The effectiveness of these traditional diagnostic systems heavily relies on the precision of the hand-crafted feature extraction techniques and the efficiency of the classification algorithms [24]. Specifically, crafting optimal and robust features for tumor identification presents significant challenges due to inherent limitations such as interclass variations and deformations within mammographic images. However, with the introduction of domain-specific hardware architectures, such as GPUs and TPUs, and the consequent advancements in computational power, deep learning-based classifiers have surged in popularity for breast cancer detection [29]. These modern approaches leverage the ability of deep learning models to automatically learn feature representations from mammograms, bypassing the need for manual feature extraction. This shift towards deep learning has revolutionized the Computer-Aided Diagnosis (CAD) system for breast cancer detection, enabling the development of models that are not only more accurate but also capable of handling the complex variations found in breast tissues and tumor appearances [30]. In the domain of BCD, the literature describes deep learning-based techniques for the analysis of mammogram images. [PERSON] et al. [5] have made a significant contribution with their YOLO-based fusion model, designed for the classification and detection of breast cancer using mammogram images. Their methodology involved augmenting the training dataset and employing a convolutional neural network (CNN), specifically the DarkNet model, in various configurations. The most efficient configuration, termed the fusion model, demonstrated promising results in breast cancer detection when evaluated using the CBIS-DDSM and INBreast datasets. However, this approach has limitations--notably, the YOLO model's low suitability for mammogram images and the fact that the backbone CNN ignores multiscale features [31], which are vital for early-stage lesion detection and handling class variations. [PERSON] et al. [11] proposed a CAD system for mammogram analysis based on YOLOv3, a deep-learning model. Their system encompasses three key stages: preprocessing, application of the YOLOv3 model, and evaluation. The model is distinct in its dual capability of locating masses and classifying various categories such as microcalcification, mass, benign, and malignant lesions. They developed three specialized training models using the CBIS-DDSM dataset: a general model using all images, a mass-specific model, and a microcalcification-focused model. [PERSON] et al. [19] proposed a multiscale method for early-stage BCD, utilizing DenseNet and MobileNet as baseline CNN architectures. Their novel approach replaced the last polling layer of the baseline CNN with a multiscale module and incorporated a Breast Region Segmentation (BRS) module for image preprocessing. Evaluating their model using the INBreast benchmark, they found DenseNet to outperform MobileNet.