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@article{187148,
        author = {Tarun Lambhate and Kamlendra Verma},
        title = {Combined Deep Learning and Machine Learning Approach for Histopathological Image-Based Cancer Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4822-4825},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187148},
        abstract = {Cancer remains one of the leading causes of death globally, with early detection being critical for improving patient outcomes. Histopathological imaging, which involves the inspection of tissue samples under a microscope, plays a crucial role in cancer diagnosis. However, manual analysis of these images is effortful, time-consuming, and prone to human error. This paper proposes a hybrid approach that combines deep learning (DL) and machine learning (ML) techniques to automate the detection and classification of cancer, particularly leukemia, using histopathological images. The proposed system influences the ResNet-50 architecture for feature extraction and integrates traditional ML classifiers such as Support Vector Machines (SVM) and Random Forest for robust decision-making. The hybrid model achieves a classification correctness of 97.9%, outperforming existing models. The results demonstrate the potential of this approach to make better early cancer detection, reduce diagnostic errors, and enhance the efficiency of histopathological image analysis.},
        keywords = {Early detection, Histopathological imaging, Cancer diagnosis, Hybrid approach, Deep learning (DL), Machine learning (ML), Leukemia, ResNet-50 architecture Support Vector Machines (SVM), Random Forest.},
        month = {November},
        }