The International Arab Journal of Information Technology (IAJIT)


Densely Convolutional Networks for Breast Cancer Classification with Multi-Modal Image Fusion

Breast cancer is the main health burden worldwide. Cancer is located in the breast, starts when the cell grows under control and begins as in-situ carcinoma and when spread into other parts known as invasive carcinoma. Breast cancer mass can early be found by image modality when discovering mass early can easily diagnose and treated. Multimodalities used for the classification of breast cancer Such as mammography, ultrasound, and Magnetic resonance imaging. Two types of fusion are used earlier fusion and later fusion. Early fusion it’s a simple relation between modalities while later fusion gives more interest to fusion strategy to learn the complex relationship between various modalities as a result, can get highly accurate results when using the later fusion. When combining two image modalities (mammography, ultrasound) and using an excel sheet containing the age, view, side, and status attribute associated with each mammographic image using DenseNet 201 with Layer level fusion strategy as later fusion by making connections between the various paths and same path by using Concatenated layer. Fusing at the feature level achieves the best performance in terms of several evaluation metrics (accuracy, recall, precision area under the curve, and F1 score) and performance.

[1] Al-Dhabyani W., Gomaa M., Khaled H., and Fahmya A., “Deep Learning Approaches for Data Augmentation and Classification of Breast Masses Using Ultrasound Images,” Journal of Advanced Computer Science and Applications, vol. 10, no. 5, pp. 1-11, 2019.

[2] Dolz J., Gopinath K., Yuan J., Lombaert H., Desrosiers C., and Ben Ayed I., “HyperDense- Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation,” IEEE Transactions on Medical Imaging, vol. 38, no. 5, pp. 1116-1126, 2019.

[3] Fujioka T., Kubota K., Mori M., Kikuchi Y., Katsuta L., Kasahara M., Oda G., Ishiba T., Nakagawa T., and Tateishi U., “Distinction Between Benign and Malignant Breast Masses at Breast Ultrasound Using Deep Learning Method With A Convolutional Neural Network,” Japanese journal of radiology, vol. 37, no. 6, pp. 466-472 2019.

[4] Garg S. and Jindal B., “Skin Lesion Segmentation in Dermoscopy Imagery,” The International Arab Journal of Information Technology, vol. 19, no. 1, pp. 29-37, 2022.

[5] Guo Z., Li X, Huang H., Guo N., and Li Q., “Medical Image Segmentation Based on Multi- Modal Convolutional Neural Network: Study on Image Fusion Schemes,” in Proceeding of the IEEE 15th International Symposium on Biomedical Imaging, USA, pp. 903-907, 2018.

[6] Hamdy E., Zaghloul M., and Badawy O., “Deep Learning Supported Breast Cancer Classification with Multi-Modal Image Fusion,” in Proceeding of the 2021 22nd International Arab Conference on Information Technology, Oman, pp. 1-7, Densely Convolutional Networks For Breast Cancer Classification With Multi-Modal Image Fusion 469 2021.

[7] Huang G., Liu Z., Maaten L., and Weinberger K., “Densely Connected Convolutional Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA, pp. 4700-4708 2017.

[8] Kaggle. s2, Last Visited, 2021.

[9] Shen L., Margolies L., Rothstein J. Fludere., McBride R., and Sieh W., Reports S., “Deep Learning to Improve Breast Cancer Detection on Screening Mammography,” Scientific Reports, vol. 9, no. 1, pp. 1-12, 2019.

[10] Singh A., Demedicalizing Women’s Health, New Delhi, Gyan Publishing House, 2010.

[11] Zhou T., Ruan S. and Canu S., “A review: Deep Learning for Medical Image Segmentation Using Multi-Modality Fusion,” Array vol. 3, pp. 100004, 2019.