Breast cancer detection AI/sbkinews.in
Solan, Himachal Pradesh: In a groundbreaking advancement, researchers at Shoolini University have developed an advanced machine learning model that promises to simplify breast cancer detection drastically. After five years of intensive research, the team has created a system boasting an impressive accuracy rate ranging between 88% and 99.6%, making early diagnosis more reliable, affordable, and accessible.
A Revolutionary Step in Cancer Diagnosis
Traditionally, breast cancer diagnosis has involved multiple tests, including costly and time-consuming hospital examinations, following initial scans such as CT (Computed Tomography). According to Dr. Gaurav Gupta, Associate Professor in the Department of Computer Science, the new machine learning software enables rapid analysis of CT scan data through computer systems — reducing diagnostic delays and the need for additional invasive tests.
The system, initially developed for desktop computer systems, is also in the pipeline for mobile adaptation, making it easier for doctors and patients to access diagnostic results remotely. This could be a significant boon for rural and underserved regions where specialized cancer diagnostic services are scarce.
The Research Team and Technological Framework
The research was led by Dr. Gaurav Gupta alongside Assistant Professor Dr. Bharti Thakur, Research Fellow Dr. Shivani Bhardwaj, and student Abdullahi Mohammed. Together, they developed an advanced ensemble model incorporating three powerful machine learning techniques: Support Vector Machines (SVM), Logistic Regression, and K-Nearest Neighbors (KNN).
This ensemble approach optimizes the strengths of each algorithm, resulting in highly accurate, reliable detection outcomes. The model was trained and tested on over 100,000 CT scan datasets, utilizing sophisticated feature selection methods to enhance prediction performance.
Impact on Early Detection and Treatment
Early detection of breast cancer dramatically increases the survival rate and simplifies treatment procedures. By making diagnosis quicker and more affordable—estimated to cost around ₹1,000 compared to the current ₹8,000 to ₹10,000—this innovation could revolutionize breast cancer care in India.
Dr. Gupta explains that quicker diagnostic reports also mean earlier intervention, reducing emotional stress for patients and healthcare providers alike. The model aims to empower healthcare professionals to make informed decisions with greater confidence.
Addressing Regional Health Challenges
Breast cancer remains a leading cause of mortality among Indian women, with Uttar Pradesh reported as the most affected state, recording 29,573 cases as of 2020 according to the Indian Council of Medical Research. Early diagnosis tools like this machine learning system are urgently needed to curb rising rates and provide affordable care in resource-limited settings.
Shoolini University’s innovation stands as a beacon of hope, especially for women in rural and semi-urban areas, where accessibility and affordability of cancer diagnostics remain significant barriers.
Breast cancer detection AI/sbkinews.in
Future Prospects and Mobile Integration
Plans are underway to launch a mobile version of the software, widening accessibility. By integrating the model into mobile health applications, the diagnostics can reach remote areas rapidly, enabling community health workers and patients to perform preliminary scans and receive timely evaluations.
Such mobile deployment aligns well with national health missions aiming to digitize and democratize healthcare delivery across India.
About the Machine Learning Model
Machine learning models operate through algorithms—sequential computational steps analyzing data patterns to make predictions. Shoolini University’s team curated an ensemble model combining different algorithmic strengths to improve accuracy across various statistical metrics like precision, recall, and F1 score.
This pioneering use of machine learning algorithms for breast cancer diagnosis is a first in India, placing Shoolini University at the forefront of healthcare technology innovation.
Affordability and Accessibility: Key Benefits
The drastically reduced cost of diagnosis holds particular significance for low-income populations. Currently, breast cancer detection in India involves high costs that may discourage early check-ups. This new model promises to democratize cancer screening, enabling more women to seek timely medical help without financial burden.
Moreover, the integration with CT scan machines and computers means health facilities do not require expensive new equipment, reducing infrastructure costs.
The Path Forward
This research highlights the critical synergy between technological innovation and healthcare improvements. Shoolini University continues to engage with government health programs and private healthcare providers to deploy this technology at a wider scale.
Furthermore, educational initiatives accompanying the technology aim to raise awareness about breast cancer signs, preventive screenings, and lifestyle factors contributing to risk reduction.
Conclusion
Shoolini University’s cutting-edge machine learning model offers a promising new chapter in the fight against breast cancer in India. By providing fast, accurate, and affordable detection, it addresses longstanding challenges of accessibility and cost, ultimately improving early diagnosis and treatment outcomes. With mobile integration on the horizon and continued collaboration with health sectors, this technology embodies hope for millions of women battling breast cancer.
According to a detailed report by Jagran newspaper, scientists at Shoolini University have developed a machine learning model that greatly simplifies and improves breast cancer detection accuracy.
For more health news and updates, visit our Health Page.
