(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 7, 2022 Breast Cancer Detection and Classification using Deep Learning Xception Algorithm Basem S. Abunasser 1 , Mohammed Rasheed J. AL-Hiealy 2 , Ihab S. Zaqout 3 , Samy S. Abu-Naser 4 University Malaysia of Computer Science & Engineering (UNIMY), Cyberjaya, Malaysia 1, 2 Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine 3, 4 Abstract—Breast Cancer (BC) is one of the leading cause of deaths worldwide. Approximately 10 million people pass away internationally from breast cancer in the year 2020. Breast Cancer is a fatal disease and very popular among women globally. It is ranked fourth among the fatal diseases of different cancers, for example colorectal cancer, cervical cancer, and brain tumors. Furthermore, the number of new cases of breast cancer is anticipated to upsurge by 70% in the next twenty years. Consequently, early detection and precise diagnosis of breast cancer plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, Magnetic Resonance Imaging (MRI), computed tomography (CT) images. The aim of this study is to propose a deep learning model to detect and classify breast cancers. Breast cancers has eight classes of cancers: benign adenosis, benign fibroadenoma, benign phyllodes tumor, benign tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. The dataset was collected from Kaggle depository for breast cancer detection and classification. The measurement that was used in the evaluation of the proposed model includes: F1-score, recall, precision, accuracy. The proposed model was trained, validated and tested using the preprocessed dataset. The results showed that Precision was (97.60%), Recall (97.60%) and F1-Score (97.58%). This indicates that deep learning models are suitable for detecting and classifying breast cancers precisely. Keywords—Breast cancer; deep learning; xception I. INTRODUCTION Breast Cancer is considered one of the most common type of cancers and is taken as the second, to cause death in women worldwide. In 2002, it was the second most common cancer globally, by means of exceeding of one million new cases. Despite the enhancements in early detection and knowing of the molecular foundations of the biology of the breast cancer, nearly 30% of the patients with “early-stage” breast cancer have disease recurred. Determining the most real and least toxic therapy, molecular and clinical features of the tumor in certain need of inspection. General treatment of breast cancer comprises hormonal agents, immunotherapy and cytotoxic. These medications are used in adjuvant, transitional, and neoadjuvant modes. Overall, systemic agents are vigorous at the start of management in 90% of main breast cancers and 50% of metastases. Though, after a flexible period of time, progress does follow. At the present, opposition to treatment is not only popular but anticipated [7, 9]. Diagnosing BC early is the most important features of its treatment. Amongst the diverse types of diagnosing technique of BC, imaging is the main diagnostic method that can make offer important data on the patients of BC. It was revealed that a few techniques of imaging e.g.: “Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT), mammography, Computerized Tomography (CT), and Positron Emission Tomography (PET)” can be put into use for the identifying and observing of the patients of BC in diverse stages. Moreover, techniques of imaging, and the use of biomarkers of biochemical e.g.: “proteins, mRNAs, DNA, and microRNAs” can be used as an innovative analytic and therapy gears for BC patients [12, 13 and 14]. Deep Learning (DL) as a type of Machine Learning method has its working mechanism inspiration from the way human brain neurons process information. The most basic element of the DL networks are small nodes known as artificial neurons , which are usually arranged in layers wherein each neuron has connections to every neuron in the subsequent layer via weighted connections. Recently, the rise of DL technique has encouraged different areas of study to solve complex problem or enhance performances of existing study using the new technology. Example of application of DL includes machine translation, speech recognition, sentiment analysis, image recognition, face recognition, signal processing, etc. [17]. The current trend of applying DL technique in medical applications has reaped great success with the potential of DL technology to perform faster analysis with higher accuracy when compared with human practitioners. To give an example, a notable study by Google on the diagnostic classification of diabetic retinopathy has shown remarkable performance that exceeds the capabilities of domain experts [22]. In addition, the application of transfer learning techniques can be seen in several studies. As opposed to training a model from scratch, transfer learning method allowed the use of weight s trained previously on a specific task to be reused as the starting point for a model on another task. It comes with the benefits of shorter training time and is possible to deliver better results. CNN models such as Alex Net, VGG, ResNet, and Xception are the most dominant models applied in transfer learning approach. Generally, there are two ways of applying transfer learning technique; First, pre-trained model with weights trained on ImageNet dataset 223 | Page www.ijacsa.thesai.org