• Ms. Sravani Yelisetty
• Mrs. Dr.B.Prasad

Brain tumor detection is a critical aspect of medical imaging, essential for early diagnosis and treatment planning. Convolutional Neural Networks (CNNs) have proven to be highly effective in various computer vision tasks, including medical image analysis. In this research, we present an algorithm based on CNNs for the detection of brain tumors using magnetic resonance imaging (MRI) scans. The proposed algorithm employs a deep CNN architecture with multiple convolutional and pooling layers to automatically extract pertinent features from MRI images. Data pre-processing, including skull stripping and intensity normalization, is applied to enhance the network's performance. The trained CNN model exhibits significant accuracy and sensitivity in distinguishing between tumor and non-tumor regions in brain MRI scans. Our proposed approach shows promising results and serves as a valuable tool for radiologists and clinicians, facilitating early detection and diagnosis of brain tumors. The existing offering process is susceptible to vulnerabilities that can negatively impact project delivery. Previous studies have extensively explored memory management issues associated with large datasets; however, these studies have not proposed solutions for problem analysis or mitigation. Additionally, there has been a lack of examination into the efficiency of the data maintenance process itself. This project aims to identify and analyze issues at each stage of the public tendering process, offering potential solutions to address or mitigate these concerns. The vendor selection process for subcontracting projects or purchasing project-related goods and services is conducted through the bidding process. Bid records contain specifications for the project or information about the goods and services to be procured. In this project, we consolidate all sensitive and large-volume data provided by various stakeholders in the bidding process. Instead of relying on traditional approaches in big data systems, we employ a divide-and-retrieve approach. A prominent issue in the bidding system is its inability to provide a comprehensive contractor database containing information about their personnel, previous works, experiences, and performance evaluations. Another significant factor to consider is the scarcity of human resources, both in terms of quantity and expertise. The project aims to address these challenges and enhance the efficiency of the public tendering process.

Brain Tumor Detection,CNN Architecture, Data Pre-Processing, Magnetic Resonance Imaging.

"Brain Tumor Detection Using CNN Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e328-e332, February-2024, Available :http://www.jetir.org/papers/JETIR2402447.pdf

"Brain Tumor Detection Using CNN Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe328-e332, February-2024, Available at : http://www.jetir.org/papers/JETIR2402447.pdf