Mammary Tumor Screening

Abstract

Mammary Tumor Screening using deep learning provides an innovative approach for early breast cancer detection. In this work, a model trained on Convolutional Neural Networks (CNNs) on the Kaggle Multi Cancer dataset, consisting of 10,000 high-resolution histopathological images of benign and malignant tumors. To improve model performance and lessen overfitting, preprocessing methods like resizing, normalisation, and data augmentation are used. The CNN model .The CNN model is designed for binary classification, and itsF1-score, recall, accuracy, and precision are used to assess performance. This inquiry seeks to fashion a faultless core, employing an exhaustive dataset used by a cancer detection system. The high-resolution dataset comes from Kaggle, consisting of histopathological images of both benign and malignant growths, specifically malignant breast tumors. It furnishes multiple images to procure thorough diagnostic evaluations quickly. Experimental results show high effectiveness, implying a high level of helpfulness. Every instance of breast cancer being detected early improves patient outcomes

Introduction

Breast cancer remains one of the leading causes of cancer-related deaths globally, emphasizing the urgent need for accurate and early detection methods. Traditional diagnostic tools like mammography and ultrasound, while essential, often face challenges such as delays, human error, and limitations in detecting tumors in dense breast tissue. Recent advances in artificial intelligence, particularly deep learning and Convolutional Neural Networks (CNNs), offer promising improvements in automating and enhancing the accuracy of breast cancer diagnosis through analysis of histopathological images.

This study develops a deep CNN model for mammary tumor screening using the Kaggle Multi Cancer dataset, which contains 10,000 high-resolution images of benign and malignant tumors. The approach includes image preprocessing, normalization, data augmentation, and rigorous model training for binary classification, achieving improved diagnostic accuracy and speed. The model is deployed via a cloud-based platform with a user-friendly graphical interface, enabling healthcare professionals to obtain rapid and reliable tumor classifications remotely.

The literature review highlights the limitations of existing screening methods like mammography, the potential of integrating thermal imaging with AI, and the growing role of explainable AI to improve transparency and trust in diagnostic models. Various machine learning techniques, including Support Vector Machines, Random Forest, and advanced CNN architectures (e.g., ResNet, EfficientNet), have shown high accuracy in tumor classification. Preprocessing methods and augmentation strategies play a critical role in enhancing model robustness.

The methodology involves careful data handling, resizing images to uniform dimensions, employing transfer learning, and using performance metrics such as accuracy, precision, recall, and F1 score for evaluation. The final system is designed for practical clinical use, offering a scalable and accessible tool to aid early detection and reduce breast cancer mortality.

Conclusion

In conclusion, this innovative approach holds great promise and analysis of image is very powerful procedure to recognize the existence of breast cancer. Continued research, for optimal results, the integration of new modern techniques on diverse datasets to enhance detection of accuracy, and for better outcomes.For advanced imaging technologies such as ultrasound, histopathology images, mammograms, and many more for high accuracy. The accurate categorizing of tumors as benign or malignant remarkably upgrade early diagnosis and treatment planning.

References

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Copyright

Copyright © 2025 Prof. Santusti Betgeri, Manali Dubla, Manasi Patil, Tanishq Pardeshi, Ashumal Palde. 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.