CNN-Based Deep Learning Model for Detecting Regions of Interest in Breast Cancer Images
DOI:
https://doi.org/10.32628/IJSRST251392Keywords:
CNN, Cancer detection, Dataset, SVM classificationAbstract
Early identification of breast cancer is high in priority to accelerate the survival rate of women with the improved outcome of treatment. This paper explores the development of a deep learning framework to classify from the detected images of breast cancer. Previous algorithms involve multiple sub-functions, leading to increased computational time with higher complexity. This research introduces a modified deep learning structure to reduce the computational complexity of cancer image detection. The proposed framework operates with a higher number of unseen layers and epochs. The algorithm of CNN is employed for feature extraction from cancer images, and the standard IBM cancer image dataset is used to test the system. Since the dataset is fully labelled, training is executed on the cancer images. Classification is performed using a support vector classifier, achieving a result of 99.78% accuracy, which surpasses that of other conventional methods
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S.P. Deshmukh, D. Choudhari, S. Amalraj, P.N. Matte, Hybrid Deep Learning Method for Detection of Liver Cancer, Comput. Assist. Methods Eng. Sci. 30 (2023) 151–165. https://doi.org/10.24423/cames.463.
C. Mannelli, Tissue vs Liquid Biopsies for Cancer Detection: Ethical Issues, J. Bioeth. Inq. 16 (2019) 551–557. https://doi.org/10.1007/s11673-019-09944-y. DOI: https://doi.org/10.1007/s11673-019-09944-y
K.R. Kasture, W. V. Patil, A. Shankar, Comparative Analysis of Deep Learning Models for Early Prediction and Subtype Classification of Ovarian Cancer: A Comprehensive Study, Int. J. Intell. Syst. Appl. Eng. 12 (2024) 507–515.
Y.T. Margaret Lee, Patterns of metastasis and natural courses of breast carcinoma, Cancer Metastasis Rev. 4 (1985) 153–172. https://doi.org/10.1007/BF00050693. DOI: https://doi.org/10.1007/BF00050693
Y. Hüsemann, J.B. Geigl, F. Schubert, P. Musiani, M. Meyer, E. Burghart, G. Forni, R. Eils, T. Fehm, G. Riethmüller, C.A. Klein, Systemic Spread Is an Early Step in Breast Cancer, Cancer Cell. 13 (2008) 58–68. https://doi.org/10.1016/j.ccr.2007.12.003. DOI: https://doi.org/10.1016/j.ccr.2007.12.003
G.T. Kennedy, F.S. Azari, E. Bernstein, B. Nadeem, A. Chang, A. Segil, N. Sullivan, E. Encarnado, C. Desphande, J.C. Kucharczuk, K. Leonard, P.S. Low, S. Chen, A. Criton, S. Singhal, Targeted detection of cancer cells during biopsy allows real-time diagnosis of pulmonary nodules, Eur. J. Nucl. Med. Mol. Imaging. 49 (2022) 4194–4204. https://doi.org/10.1007/s00259-022-05868-9. DOI: https://doi.org/10.1007/s00259-022-05868-9
V. Korde & N. Patil, Effect of methyl alcohol and nitric acid as an etchant on the motions of ferroelectric domains in Al-doped KNbO3 single crystal. Materials Research Innovations, 24(5), (2019) 263–269. https://doi.org/10.1080/14328917.2019.1646948 DOI: https://doi.org/10.1080/14328917.2019.1646948
S. H. Shamkuwar, N. Patil, V. Korde, N. Pradnyakar, Dielectric and domain studies on Fe doped KNbO3 single crystal, AIP Conf. Proc. 1953, (2018) 070014. DOI: https://doi.org/10.1063/1.5032792
M. Riis, Modern surgical treatment of breast cancer, Ann. Med. Surg. 56 (2020) 95–107. https://doi.org/10.1016/j.amsu.2020.06.016. DOI: https://doi.org/10.1016/j.amsu.2020.06.016
A. Fredderics, K. Vinoth Kumar, A. Shankar, J.S. Renius, R. Guru, S.L. Nair, K. Viswanathan, The FHA analysis of dual-bridge LLC type resonant converter, Int. J. Power Electron. Drive Syst. 4 (2014) 538–546. https://doi.org/10.11591/ijpeds.v4i4.6513. DOI: https://doi.org/10.11591/ijpeds.v4i4.6513
V.B. Korde, S. Khot, D.B. Kamble, S. Amalraj, Review: Perovskite nanostructures materials versatile platform for advance biosensor applications, Sensors and Actuators Reports. 7 (2024) 100201. https://doi.org/10.1016/j.snr.2024.100201.
P. Ramesh, J. Pitchaimani, S. Amalraj, R. Koutavarapu, One-pot eco-friendly synthesis and supercapacitor applications: Simultaneously oxidation and intercalation of iodinated graphene oxide, Diam. Relat. Mater. 127 (2022) 109140. https://doi.org/10.1016/j.diamond.2022.109140. DOI: https://doi.org/10.1016/j.diamond.2022.109140
M.S. Dhore, A.N. Bondre, C. Behera, S. Amalraj, Synthesis and spectral study of 2, 4-Diaminophenol based copolymer, J. Phys. Conf. Ser. 1913 (2021). https://doi.org/10.1088/1742-6596/1913/1/012060. DOI: https://doi.org/10.1088/1742-6596/1913/1/012060
N. Vijaya, S.D.A. Sujitha, A. Shankar, S. Khot, C.B. Kanna, M.S. AlSalhi, T. Selvankumar, S.C. Kim, M. Santhamoorthy, P. Ramesh, ZIF-67 derivatives of NiCo2S4 Decorated in Salen-Complex Amine Functionalization GO Layers for High-Performance Applications in Supercapacitor Devices, Luminescence. 39 (2024). https://doi.org/10.1002/bio.70018. DOI: https://doi.org/10.1002/bio.70018
Vivek B. Korde, Ashish B. Khelkar, Suhas Khot, Prakash Malavadakar, Prajakta Deshmukh, Shankar Amalraj, Advancements of lithium-ion battery Recycling: Transitioning from traditional methods to AI and machine learning techniques, Renewable and Sustainable Energy Reviews, 225 (2026) 116180. https://doi.org/10.1016/j.rser.2025.116180 DOI: https://doi.org/10.1016/j.rser.2025.116180
Vivek B. Korde, Naresh M. Patil, Domain imaging in Fe-doped KNbO3 single crystal via trinocular microscopy and scanning electron microscopy, Materials Chemistry and Physics, 226 (2019) 230-234. https://doi.org/10.1016/j.matchemphys.2018.12.071 DOI: https://doi.org/10.1016/j.matchemphys.2018.12.071
S.S. Saniganti, S.R. Gaddam, S.R. Eppa, S.K. Rout, B.K. Sethi, B. Kethireddy, Breast Cancer Detection Using Convolutional Neural Network, 2024 2nd Int. Conf. Disruptive Technol. ICDT 2024. (2024) 1409–1414. https://doi.org/10.1109/ICDT61202.2024.10489574. DOI: https://doi.org/10.1109/ICDT61202.2024.10489574
A. Seemendra, R. Singh, S. Singh, Breast Cancer Classification Using Transfer Learning, Lect. Notes Electr. Eng. 694 (2021) 425–436. https://doi.org/10.1007/978-981-15-7804-5_32. DOI: https://doi.org/10.1007/978-981-15-7804-5_32
S. Amalraj, P.A. Michael, Synthesis and characterization of Al2O3 and CuO nanoparticles into nanofluids for solar panel applications, Results Phys. 15 (2019). https://doi.org/10.1016/j.rinp.2019.102797. DOI: https://doi.org/10.1016/j.rinp.2019.102797
X. Zhang, Y. Zhang, E.Y. Han, N. Jacobs, Q. Han, X. Wang, J. Liu, Whole mammogram image classification with convolutional neural networks, Proc. - 2017 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2017. 2017-Janua (2017) 700–704. https://doi.org/10.1109/BIBM.2017.8217738. DOI: https://doi.org/10.1109/BIBM.2017.8217738
G. Piantadosi, M. Sansone, R. Fusco, C. Sansone, Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks, Artif. Intell. Med. 103 (2020) 101781. https://doi.org/10.1016/j.artmed.2019.101781. DOI: https://doi.org/10.1016/j.artmed.2019.101781
M.D. Ali, A. Saleem, H. Elahi, M.A. Khan, M.I. Khan, M.M. Yaqoob, U. Farooq Khattak, A. Al-Rasheed, Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks, Diagnostics. 13 (2023). https://doi.org/10.3390/diagnostics13132242. DOI: https://doi.org/10.3390/diagnostics13132242
L. Abdelrahman, M. Al Ghamdi, F. Collado-Mesa, M. Abdel-Mottaleb, Convolutional neural networks for breast cancer detection in mammography: A survey, Comput. Biol. Med. 131 (2021) 104248. https://doi.org/10.1016/j.compbiomed.2021.104248. DOI: https://doi.org/10.1016/j.compbiomed.2021.104248
S. Dabeer, M.M. Khan, S. Islam, Cancer diagnosis in histopathological image: CNN based approach, Informatics Med. Unlocked. 16 (2019) 100231. https://doi.org/10.1016/j.imu.2019.100231. DOI: https://doi.org/10.1016/j.imu.2019.100231
A.P. Tarighat, Breast Tumor Segmentation Using Deep Learning by U-Net Network, (2021).
D.K. Gurmessa, W. Jimma, Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review, BMJ Heal. Care Informatics. 31 (2024) 1–10. https://doi.org/10.1136/bmjhci-2023-100954. DOI: https://doi.org/10.1136/bmjhci-2023-100954
S. Goyal, A. Shankar, K.C. Das, A. Singh, S. Oli, M.M. Sati, Manual and Adaptive tuned PID controllers for industrial application, 2024 4th Int. Conf. Adv. Comput. Innov. Technol. Eng. ICACITE 2024. (2024) 1953–1958. https://doi.org/10.1109/ICACITE60783.2024.10616874. DOI: https://doi.org/10.1109/ICACITE60783.2024.10616874
Vivek B. Korde, Suhas Khot, Dinkar B. Kamble, Shankar Amalraj, Review: Perovskite nanostructures materials versatile platform for advance biosensor applications, Sensors and Actuators Reports, 7 (2024) 100201. https://doi.org/10.1016/j.snr.2024.100201 DOI: https://doi.org/10.1016/j.snr.2024.100201
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