Deep Learning-Based Cancer Detection Using Machine Learning

Abstract

This area under reference is concerned with such a part of artificial intelligence that receives artificial neural networks as machine learning techniques to recognize patterns or predict from very large data sets. The enthusiastic adoption of deep learning in the healthcare sector, and accordingly, the availability of deeply well-characterized datasets for the cancer prognosis have made the field much more electrifying in applying deep learning tools to unravel cancer complex biology. Early findings are promising, but it remains an ever-changing world where information on cancer biology and deep learning keeps pouring in. This research offers an insight into the latest techniques in deep learning and their applications in oncology. Focus will mainly be placed on the applications of deep learning towards omics data types-on genomic, methylation, transcriptomic data, histopathology-inductive genomic inference, and provide commentaries on perspectives toward integrating these data types into decision support tools. Finally, examples for applying deep learning to cancer diagnosis, treatment, and management will be illustrated. In addition, we will highlight challenges and limitations to applying deep learning in precision oncology: the scarce availability of data, especially of phenotype-rich type, and the need for more interpretable deep learning models. At last, scope shall give a view of how these challenges can be fought with and sorted out in prospect for the future clinical application of deep learning.

Introduction

The integration of deep learning (DL) with health systems is rapidly transforming cancer diagnostics and treatment by analyzing vast, complex biological data such as genomics, transcriptomics, methylation, and histopathology. Deep learning, particularly artificial neural networks, excels at uncovering hidden molecular patterns and biomarkers that aid early and accurate cancer detection, personalized treatment, and improved patient outcomes. Applications in histopathology have enhanced tissue analysis, sometimes outperforming human experts.

However, challenges remain, including the scarcity of large, well-annotated, phenotypically rich datasets and the “black box” nature of DL models, which complicates clinical interpretability and trust. Effective clinical adoption requires collaboration to develop predictive, interpretable, and feasible models.

The study focuses on cervical, breast, and lung cancers, proposing a system combining multi-omics data and histopathological images in a deep learning framework (using convolutional neural networks) to improve diagnostic accuracy and therapy personalization. The methodology involves preprocessing diverse datasets, integrating features, training CNN models, and addressing issues like overfitting through regularization and data augmentation.

While DL shows great promise in precision oncology, overcoming data limitations and enhancing model interpretability are critical steps toward fully realizing its clinical potential.

Conclusion

Simply put, it is the integration of the deep learning techniques with oncology that will carve the way for its revolutionary role in cancer diagnostics, treatment, and management of the future. Already deep learning models can analyze incredibly huge complex datasets such as genomic, methylome, transcriptomic, and histopathology modalities to provide quite promising insights into complicated biological behavior in cancer. New knowledge on cancer biology and deep learning methods can quickly improve the emerging developments in cancer diagnosis as the strong push of its evolution continues. However, some important barriers still exist for their clinical acceptance, such as the lack of rich, phenotypic data and interpretable models. Innovative approaches will be required for such obstacles, for example, formulating and integrating various forms of omics data and development of interpretable models to ensure success of application in precision oncology by deep learning. From this perspective, it is expected that continued research will make deep learning a more important vehicle for personalized cancer care, thus improving the quality of cancer treatments for a specific patient in the future.

References

[1] N. Lee, G. Márquez, J. M. Levsky, and J. K. Gohagan, “Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening,” [2] J. Tang, R. M. Rangayyan, J. Xu, I. E. El Naqa, and Y. Yang, Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances, IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 236–251, 2009, doi: 10.1109/TITB.2008.2009441. [3] L. Song, W. Hsu, J. Xu, and M. Van Der Schaar, “Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening,”.2015.2414934. [4] N. Yukinawa, S. Oba, K. Kato, and S. Ishii, Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles,.70239. [5] M. Li et al., Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on HistopathologicalImages,doi:10.1109/ACCESS.2021.3071057. [6] R. Lupat, R. Perera, S. Loi, and J. Li, “Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes,” doi: 10.1109/ACCESS.2023.3240515. [7] F. Cristovao et al., “Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data,” IEEE/ACM Trans ComputBiolBioinform, vol. 19, no. 1, pp. 121–134, 2022, doi: 10.1109/TCBB.2020.3042309. [8] M. Aharonu and R. Lokesh Kumar, “A Multi-Model Deep Learning Framework and Algorithms for Survival Rate Prediction of Lung Cancer Subtypes with Region of Interest Using Histopathology Imagery,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3484495. [9] R. Halawani, M. Buchert, and Y. P. P. Chen, “Utilizing Neurons to Interrogate Cancer: Integrative Analysis of Cancer Omics Data with Deep Learning Models,” IEEE Rev Biomed Eng, 2024, doi: 10.1109/RBME.2024.3503761. [10] X. Wang et al., Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis,, doi: 10.1109/TCYB.2019.2935141 [11] S. Iqbal et al., Prostate Cancer Detection Using Deep Learning and Traditional Techniques [12] X. Zhang et al., “Deep Learning Based Analysis of Breast Cancer Using Advanced Ensemble Classifier and Linear Discriminant Analysis,” IEEE Access, vol. 8, pp. 120208–120217, 2020, doi: 10.1109/ACCESS.2020.3005228. [13] J. Zheng, D. Lin, Z. Gao, S. Wang, M. He, and J. Fan, “Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis,” IEEE Access, vol. 8, pp. 96946–96954, 2020, doi: 10.1109/ACCESS.2020.2993536. [14] Y. Yari, T. V. Nguyen, and H. T. Nguyen, “Deep learning applied for histological diagnosis of breast cancer,” IEEE Access, vol. 8, pp. 162432–162448, 2020, doi: 10.1109/ACCESS.2020.3021557. [15] F. Azour and A. Boukerche, Design Guidelines for Mammogram-Based Computer-Aided Systems Using Deep Learning Techniques [16] R. Ahmed, N. Dahmani, G. Dahy, A. Darwish, and A. E. Hassanien, “Early Detection and Categorization of Cervical Cancer Cells Using Smoothing Cross Entropy-Based Multi-Deep Transfer Learning,” IEEE Access, vol. 12, pp. 157838–157853, 2024, doi: 10.1109/ACCESS.2024.3485888. [17] G. Wang et al., “Automated Ovarian Cancer Identification Using End-to-End Deep Learning and Second Harmonic Generation Imaging,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 29, no. 4, Jul. 2023, doi: 10.1109/JSTQE.2022.3228567. [18] M. Kaur, D. Singh, V. Kumar, and H. N. Lee, “MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis,” [19] M. Liu et al., A Deep Learning Method for Breast Cancer Classification in the Pathology Images, doi: 10.1109/JBHI.2022.3187765. [20] W. Arshad et al., “Cancer Unveiled: A Deep Dive Into Breast Tumor Detection Using Cutting-Edge Deep Learning Models,” IEEE Access, vol. 11, pp. 133804–133824, 2023, doi: 10.1109/ACCESS.2023.3335604. [21] J. R. Lewis, S. Pathan, P. Kumar, and C. C. Dias, “AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning Techniques.,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3483432. [22] S. Hussein, P. Kandel, C. W. Bolan, M. B. Wallace, and U. Bagci, “Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

Copyright

Copyright © 2025 Avulakunta Vamsi, Shaik Farook, Mrs. D Janani, Harekonda Sowmya, Nukathati Sreekanth, Bhukkapatnam Madhav Krishna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.