(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 9, 2021 308 | Page www.ijacsa.thesai.org Classification of Breast Cancer Cell Images using Multiple Convolution Neural Network Architectures Zarrin Tasnim 1 , F. M. Javed Mehedi Shamrat 2 , Md Saidul Islam 3 Md.Tareq Rahman 4 , Biraj Saha Aronya 5 , Jannatun Naeem Muna 6 , Md. Masum Billah 7 Department of Software Engineering, Daffodil International University, Bangladesh 1, 2, 5, 7 School of Computer and Software, Nanjing University of Information Science and Technology, China 3 Department of Computer Science and Engineering, Daffodil International University, Bangladesh 4 Department of Computer Science and Engineering, United International University, Bangladesh 6 Abstract—Breast cancer is a malignant tumor that affects women. It is the most prevalent cancer in women, affecting about 10% of all women at any point in their lives. The development of breast cancer begins in the lobules or ducts of the cells. Early detection and prevention are the best ways to stop this cancer from spreading. In this study, five Convolution Neural Network (CNN) models are used to process image data of breast cells. AlexNet, InceptionV3, GoogLeNet, VGG19 and Xception models are used for the classification of Invasive Ductal Carcinoma, IDC and Non-Invasive Ductal Carcinoma (Non-IDC) cells. The models are trained and tested at different epochs to record the learning rate. It is observed from the study that with higher epochs, the data loss decreases and accuracy increases. The accuracy of InceptionV3 and Xception is 92.48% and 90.72% respectively. Likewise, VGG19 and AlexNet have fairly close accuracy of 94.83% and 96.74%. However, GoogLeNet dominates over the other implemented models with the highest accuracy of 97.80%. The GoogLeNet model performs with high accuracy and precision in detecting IDC cells responsible for breast cancer. Keywords—Breast cancer; IDC; non-IDC; AlexNet; VGG19; Inception sV3; GoogLeNet; Xecption; accuracy I. INTRODUCTION Cancer, also known as a malignant neoplasm, is a group of more than a hundred diseases marked by irregular cell development with the ability to spread to the body's underlying tissues. IDC is a kind of breast cancer that started in the ducts of the breast and has progressed to fibrous or fatty tissue outside of the duct. IDC is the most prevalent kind of breast cancer, accounting for 80% of all occurrences. Breast cancer is the most common kind of cancer in women worldwide [1]. Many imaging techniques have been developed to aid in the early diagnosis and treatment of breast cancer, as well as the reduction of breast cancer-related mortality. To improve diagnostic precision and accuracy, many assisted breast cancer diagnosis methods have been employed [2-4]. Fig. 1 shows breast cancer cases around the world. To classify and predict breast cancer, machine learning algorithms with image processing have become quite famous for their accuracy in detecting the disease at an early stage. Ciresan et al. [5] classified each pixel into mitotic and non- mitotic groups using an 11-layered CNN. The predictions were made using likelihood ratings allocated to each pixel depending on its distance from the mitosis centroid. A related study [6] used Transfer Learning in CNNs to identify and segment brain and colon cancer images, and the findings were cutting-edge. It used AlexNet (pre-trained on ImageNet) to train a Support Vector Machine with the features extracted from the last FC layer Support Vector Machine (SVM). Gao et al. used CNN to identify interstitial lung infections [7] and discovered that a pre-trained model converged categorization faster than a randomly initialized network. It is possible to automate cell counting in microscope pictures. Weidi et al. [8] took a regression approach to the issue, which eliminates the need for previous identification or segmentation. They regressed a density surface generated by the superposition of Gaussians using completely convolutional regression networks (FCRNs). The dot annotations of each cell given as the ground truth for the training set are expressed by these Gaussians. To identify the best-supervised learning classifier, Vikas Chaurasia and Saurabh Pal [9] evaluate the performance criteria of Naive Bayes, SVM-RBF kernel, RBF neural networks, Decision trees, and basic CART in breast cancer datasets. The experimental results indicate that the SVM-RBF kernel outperforms other classifiers, scoring 96.84% accuracy in the Wisconsin Breast Cancer (original) datasets. Djebbari et al. [10] investigate the impact of an ensemble of machine learning approaches on breast cancer survival period prediction. When compared to prior results, their methodology is more accurate on their breast cancer data collection. S. Aruna and L. V Nandakishore [11] compare the findings of C4.5, Nave Bayes, Support Vector Machine (SVM), and K- Nearest Neighbor to find the appropriate classifier in WBC (K- NN). SVM is the most accurate classifier, with a 96.99% accuracy rate. Angeline Christobel. Y. [12] use a decision tree classifier (CART) to obtain an accuracy of 69.23% in breast cancer datasets. The accuracy of data mining algorithms SVM, IBK, and BF Tree is compared by A. Pradesh [13]. SMO outperforms other classifiers in terms of performance. T.Joachims [14] uses neuron fuzzy methods to reach a precision of 95.06 % by utilizing Wisconsin Breast Cancer (original) datasets. In this study, a hybrid method is proposed to increase the classification accuracy of Wisconsin Breast Cancer (original) datasets using 10-fold cross-validation. Liu Ya-Qin, W. Cheng, and Z. Lu [15] used the C5 algorithm with picking to produce additional data for training from the initial array using variations of repetitions to yield multisets of the same scale as the original data to predict breast cancer survivability. Delen et al. Lu [16] pre-classified 202,932 breast