Abstract
Identifying and diagnosing breast cancer is a well-known problem in the biomedical domain. Breast images obtained from medical scanning were taken for classifying different classes like types of breast tumour and which magnification category it belongs. Computer-aided diagnosis has gained immense progress over the past few years because of new insights and developments in deep learning. Often the performance of an efficient deep learning model is prone to drawbacks like imbalanced datasets, inconsistent annotations, less number of images, selection of inappropriate evaluation metrics, and selection of inappropriate loss functions. In this paper, we have done a comparative analysis by comparing how the deep learning model handles class imbalance problems by modifications done at the algorithmic level (i.e.) modifying the loss functions. This research lays a foundation for future research on handling imbalanced datasets and how modifications at the algorithmic level can benefit deep learning architectures.
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References
Le T, Vo MT, Kieu T, Hwang E, Rho S et al (2020) Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building. Sensors 20(9):2668
Khan ZA, Hussain T, Ullah A, Rho S, Lee MY et al (2020) Towards efficient electricity forecasting in residential and commercial buildings: a novel hybrid CNN with a LSTM-AE based framework. Sensors 20(5):1399
Ren R, Tang M, Liao H (2020) Managing minority opinions in micro-grid planning by a social network analysis-based large scale group decision making method with hesitant fuzzy linguistic information. Knowl-Based Syst 189(19):105060
Vo T, Sharma R, Kumar R, Le HS, Pham BT (2020) Crime rate detection using social media of different crime locations and twitter part-of-speech tagger with brown clustering. J Intell Fuzzy Syst 38(4):4287–4299
Lingam G, Rout RR, Somayajulu D (2019) Adaptive deep Q-learning model for detecting social bots and influential users in online social networks. Appl Intell 49(11):3947–3964
Le T, Vo B, Fujita H, Nguyen NT, Baik SW (2019) A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting. Inf Sci 494(1):294–310
Le T, Lee MY, Park JR, Baik SW (2018) Oversampling techniques for bankruptcy prediction: novel features from a transaction dataset. Symmetry 10(4):79
Hoang VL, Le HS, Khari M, Arora K, Chopra S et al (2019) A new approach for construction of geodemographic segmentation model and prediction analysis. Comput Intell Neurosci 2019(1):1–10
Wei H, Hu C, Chen S, Xue Y, Zhang Q (2019) Establishing a software defect prediction model via effective dimension reduction. Inf Sci 477(3):399–409
Doan QH, Le T, Thai DK (2021) Optimization strategies of neural networks for impact damage classification of RC panels in a small dataset. Appl Soft Comput 102:107100
Singhal P, Srivastava PK, Tiwari AK, Shukla RK (2022) A survey: approaches to facial detection and recognition with machine learning techniques. In: Gupta D, Khanna A, Kansal V, Fortino G, Hassanien AE (eds) Proceedings of second doctoral symposium on computational intelligence. Advances in intelligent systems and computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_9
Anand V, Gupta S, Koundal D (2022) Skin disease diagnosis: challenges and opportunities. In: Gupta D, Khanna A, Kansal V, Fortino G, Hassanien AE (eds) Proceedings of second doctoral symposium on computational intelligence. Advances in intelligent systems and computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_36
Tek FB, Dempster AG, Kale I (2010) Parasite detection and identification for automated thin blood film malaria diagnosis. Comput Vis Image Underst 114(1):21–32. https://doi.org/10.1016/j.cviu.2009.08.003
Cherif A, Badhib A, Ammar H, Alshehri S, Kalkatawi M, Imine A (2022) Credit card fraud detection in the era of disruptive technologies: a systematic review. J King Saud Univ Comput Inf Sci. ISSN 1319-1578. https://doi.org/10.1016/j.jksuci.2022.11.008
Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Theeramunkong T, Kijsirikul B, Cercone N, Ho TB (eds) Advances in knowledge discovery and data mining. PAKDD 2009. Lecture notes in computer science, vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_4.3
Chawla NV, Bowyer KW, Hall LO, Philip Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
Elkan C (2001) The foundations of cost-sensitive learning. In: Proceedings of the seventeenth international conference on artificial intelligence, 4–10 Aug 2001, Seattle
Elhassan T, Aljourf M, Al-Mohanna F, Shoukri M (2016) Classification of imbalance data using Tomek link (T-Link) combined with random under-sampling (RUS) as a data reduction method. Global J. Technol. Optim. 1. https://doi.org/10.4172/2229-8711.S1111
De Brébisson A, Vincent P (2016) An exploration of softmax alternatives belonging to the spherical loss family. CoRR, vol. abs/1511.05042, pp 1–9. [Online]. Available: https://arxiv.org/abs/1511.05042
Yao H, Zhu DL, Jiang B, Yu P (2019) Negative log likelihood ratio loss for deep neural network classification. In: Proceedings of the Future Technologies Conference (FTC) 2019, pp 276–282
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Swetha, A.V.S., Bala, M., Sharma, K. (2023). A Novel Multi Features Deep Learning Architecture for Breast Cancer Detection Using Loss Function. In: Swaroop, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Fourth Doctoral Symposium on Computational Intelligence . DoSCI 2023. Lecture Notes in Networks and Systems, vol 726. Springer, Singapore. https://doi.org/10.1007/978-981-99-3716-5_60
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DOI: https://doi.org/10.1007/978-981-99-3716-5_60
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