• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 18, Pages: 1385-1394

Original Article

Breast Cancer Diagnosis in Mammography Images Using Deep Convolutional Neural Network-Based Transfer and Scratch Learning Approach

Received Date:06 January 2023, Accepted Date:01 April 2023, Published Date:09 May 2023


Objectives: The study aims to utilize a Deep Convolutional Neural Network (Deep-CNN) model called MobileNetV2, which has low computational requirements, to accomplish binary classification of mammography images. To achieve this objective, the study investigates two methods: transfer learning and scratch learning. Methods: The proposed technique aims to classify mammography images from the Digital Database of Screening Mammography (DDSM) dataset into either malignant or benign categories using transfer learning and scratch learning methods based on Deep-CNN. Before being fed into the Deep-CNN, the images’ contrast level is enhanced using the min-max contrast enhancement technique. MobileNetV2, a lightweight CNN architecture, is used as a convolutional base with three transfer learning variants, including transfer learning without fine-tuning, transfer learning with fine-tuning, and fixed feature extraction, to achieve binary classification. The study also attempts to develop a seven-layered CNN architecture to accomplish mammography image classification through scratch learning. The trained breast cancer detection models’ classification performance, obtained through transfer and scratch learning methods, is evaluated based on machine learning performance metrics such as accuracy, precision, F1 Score, and area under the curve. Findings: The study conducted three transfer learning variants and found that combining MobileNetV2 with Random Forest classifier using the fixed feature extraction approach produced the best results, with an accuracy of 0.994 and a shorter training time of only 63.87 seconds compared to the other transfer learning variants. On the other hand, a seven-layer CNN model developed using scratch learning achieved a classification accuracy of 0.96, but it required a longer training period of 7980 seconds. Novelty: The breast cancer detection model introduced in this study exhibited superior performance compared to recently developed breast cancer detection models, as measured by accuracy and Area Under the Curve (AUC). The study found that the model created using the fixed feature extraction method showcases the effectiveness of utilizing MobilenetV2 and a Random Forest machine learning classifier in decreasing the number of trainable network parameters and network training time. Consequently, this presents an advantage for implementing the model on low-cost embedded platforms with limited memory size.

Keywords: Breast Cancer (BC); Computer Aided Detection (CAD); Deep Convolutional Neural Networks (DeepCNN); Transfer Learning (TL); Scratch Learning (SL)


  1. Mohapatra S, Muduly S, Mohanty S, Ravindra JVR, Mohanty SN. Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images. Sustainable Operations and Computers. 2022;3:296–302. Available from: https://doi.org/10.1016/j.susoc.2022.06.001
  2. Mahoro E, Akhloufi MA. Applying Deep Learning for Breast Cancer Detection in Radiology. Current Oncology. 2022;29(11):8767–8793. Available from: https://doi.org/10.3390/curroncol29110690
  3. Majumdar S, Pramanik P, Sarkar R. Gamma function based ensemble of CNN models for breast cancer detection in histopathology images. Expert Systems with Applications. 2023;213:119022. Available from: https://doi.org/10.1016/j.eswa.2022.119022
  4. Wakili MA, Shehu HA, Sharif MH, Sharif M, Umar A, Kusetogullari H, et al. Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning. PMCID. 2022. Available from: https://doi.org/10.1155/2022/8904768
  5. Khan MHM, Boodoo-Jahangeer N, Dullull W, Nathire S, Gao X, Sinha GR, et al. Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN) PLOS ONE. 2021;16(8):e0256500. Available from: https://doi.org/10.1371/journal.pone.0256500
  6. Houssein EH, Emam MM, Ali AA. An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm. Neural Computing and Applications. 2022;34(20):18015–18033. Available from: https://doi.org/10.1007/s00521-022-07445-5
  7. Dembrower K, Liu Y, Azizpour H, Eklund M, Smith K, Lindholm P, et al. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction. Radiology. 2020;294(2):265–272. Available from: https://doi.org/10.1148/radiol.2019190872
  8. Das A, Mohanty MN, Mallick PK, Tiwari P, Muhammad K, Zhu H. Breast cancer detection using an ensemble deep learning method. Biomedical Signal Processing and Control. 2021;70:103009. Available from: https://doi.org/10.1016/j.bspc.2021.103009
  9. Salama WM, Aly MH. Deep learning in mammography images segmentation and classification: Automated CNN approach. Alexandria Engineering Journal. 2021;60(5):4701–4709. Available from: https://doi.org/10.1016/j.aej.2021.03.048
  10. Mohapatra S, Muduly S, Mohanty S, Ravindra JVR, Mohanty SN. Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images. Sustainable Operations and Computers. 2022;3:296–302. Available from: https://doi.org/10.1016/j.susoc.2022.06.001
  11. Sahu B, Dash S, Mohanty SN, Rout SK. Ensemble Comparative Study for Diagnosis of Breast Cancer Datasets. International Journal of Engineering & Technology. 2018;7(4.15):281. Available from: https://doi.org/10.14419/ijet.v7i4.15.23007
  12. Sahu B, Mohanty SN, Rout S. A Hybrid Approach for Breast Cancer Classification and Diagnosis. ICST Transactions on Scalable Information Systems. 2019;0(0):156086. Available from: http://dx.doi.org/10.4108/eai.19-12-2018.156086
  13. Shahirahzahir A, Amir, Nik A, Zahri W, Ang C. Applying the Deep Learning Model on an IoT Board for Breast Cancer Detection based on Histopathological Images. 5th International Conference on Electronic Design (ICED) 2020 Journal of Physics: Conference Series 1755. 2021;p. 12026. Available from: https://doi.org/10.1088/1742-6596/1755/1/012026
  14. Chowdhury D, Das A, Dey A, Sarkar S, Dwivedi AD, Mukkamala RR, et al. ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning. Sensors. 2022;22(3):832. Available from: https://doi.org/10.3390/s22030832
  15. Huang J, Mei L, Long M, Liu Y, Sun W, Li X, et al. BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images. Bioengineering. 2022;9(6):261. Available from: https://doi.org/10.3390/bioengineering9060261
  16. Li C, Xu J, Liu Q, Zhou Y, Mou L, Pu Z, et al. Multi-View Mammographic Density Classification by Dilated and Attention-Guided Residual Learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2021;18(3):1003–1013. Available from: https://doi.org/10.1109/TCBB.2020.2970713


© 2023 Bokade & Shah. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee


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