ItemConference Paper

Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images

Title
Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images
Creator
Singh, Jagendra; Shaik, Nazeer; Sahu, Dinesh Prasad; Tiwari, Mohit; Alam, Mohammad Shabbir; Upreti, Kamal
Description
This article investigates machine learning techniques effectiveness at using computed tomography (CT) images to forecast breast cancer, hoping to expedite early identification and plan treatment. Drawing on many different machine learning models, such as CNN, SVM, VGG16, RNN and RF, we did extensive work to measure their performance distinguishing between malignant and benign breast tissue regions. The dataset includes 2,430 CT pictures, with 70% for training and 30% for testing. It has been carefully selected and prepared in order to guarantee robustness and consistency. The precision, and in-sensitivity measure the accuracy, sensitivity, specificity is used as analytic indicators to measure the models ability to predict regions of breast cancer accurately. Our findings show that the proposed CNN model achieved an accuracy of 98.75%, superior performance. Other machine learning models are also highlighted in this study, demonstrating how breast cancer can be predicted using various methods. This research will determine the forms and technologies suitable for breast cancer forecasting. Medical imaging and clinical decision-making can move forward because of this research, offering a glimpse into how integrated machine-learning systems can bring greater precision to diagnosis and prognosis. By careful experimentation and analysis, we hope to prepare people for early intervention and personalized treatment methods. This will make for improved patient outcomes in fighting breast cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1181;pp.455-466
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Breast cancer; Computed tomography; Convolutional neural networks; Forecasting; Machine learning
Coverage
Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shaik N., Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of TechnologyAutonomous, Anantapur, India; Sahu D.P., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Tiwari M., Department of Computer Science and Engineering, Bharati Vidyapeeths College of Engineering, Delhi, India; Alam M.S., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981978860-6;
Format
online
Language
English
Type
Conference paper

Citation

Singh, Jagendra; Shaik, Nazeer; Sahu, Dinesh Prasad; Tiwari, Mohit; Alam, Mohammad Shabbir; Upreti, Kamal, “Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25674.

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