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Sankari, M.
- Enhanced Brain Cancer Detection in MRI Scans Through Template Regression Siamese Regional Proposed Network for Segmentation and Fuzzy Logic Fusion of Segmented Regions
Authors
1 Department of Mathematics, Manonmaniam Sundaranar University, IN
2 Department of Mathematics, Lekshmipuram College of Arts and Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 15, No 1 (2024), Pagination: 3347-3356Abstract
Accurate detection and segmentation of brain cancer in MRI scans are critical for effective diagnosis and treatment planning. Traditional methods often struggle with the complexities of tumor morphology and variations in scan quality. Existing detection systems can be slow and may not effectively handle the variability in tumor appearances, leading to potential delays in diagnosis and treatment. To address these challenges, we propose an enhanced detection framework using a Siamese Regional Proposed Network (SRPN). The SRPN integrates template branch and bounding box regression to expedite detection processes. The system utilizes an extended Siamese network to learn the distance between tracklet pairs, capturing the local and global features of tumors. These features are transferred to bidirectional gated recurrent units (GRUs), which generate tracklets and segment them into shorter sub-tracklets based on local distances. The segmented subtracklets are then reconnected into longer trajectories using similarities derived from temporal pooling global features. Additionally, fuzzy logic fusion is employed to combine segmented regions for improved accuracy. The SRPN-based framework demonstrated a significant improvement in detection speed and accuracy. Experimental results show an accuracy increase of 12% over traditional methods, achieving 94% accuracy with a detection time reduction of 30%. The system also improved segmentation precision, with a mean Intersection over Union (IoU) score of 85%, compared to 75% in conventional approaches.Keywords
Brain Cancer Detection, MRI Scans, Siamese Regional Proposed Network, Bounding Box Regression, Fuzzy Logic Fusion.- Pioneering Medical Diagnosis - Neuro-Fuzzy Systems and Swarm Intelligence in Healthcare Applications
Authors
1 Department of Mathematics, Manonmaniam Sundaranar University, IN
2 Department of Mathematics, Lekshmipuram College of Arts and Science, IN
Source
ICTACT Journal on Soft Computing, Vol 15, No 1 (2024), Pagination: 3379-3385Abstract
This study introduces a hybrid approach for lung cancer detection, combining Neuro-Fuzzy Systems for robust feature extraction and the Firefly Algorithm for accurate classification of lung nodules as benign or malignant. The methodology is validated through comprehensive experiments using standard datasets and compared against established techniques like SVM-ANN and RBF-PSO. The research highlights the interpretability and learning capabilities of Neuro-Fuzzy Systems and the effectiveness of the Firefly Algorithm in medical image classification, showcasing improvements in accuracy and reliability over traditional methods.Keywords
Healthcare, Diagnosis, Medical, Fuzzy System, Swarm Intelligence- Micronutrient Levels in Saliva of Chronic Periodontitis Patients Pre and Post Non-Surgical Periodontal Therapy
Authors
1 Post Graduate Student, Department of Periodontics, Saveetha Dental College,Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IN
2 Professor, Department of Periodontics, Saveetha Dental College,Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IN
Source
Indian Journal of Public Health Research & Development, Vol 10, No 11 (2019), Pagination: 3616-3621Abstract
Background: Saliva, being the major fluid of the oral cavity is composed of various ions, electrolytes, enzymes, micronutrients and microorganisms which contribute to the homeostatic environment of the oral cavity. The micronutrients of the saliva attribute to the nature of the saliva by influencing the pH or by altering the oral microflora. The disturbance in micronutrient status and increased oxidative stress may favor periodontal disease progression. Therefore, the aim of this study was to evaluate the micronutrient levels in saliva in patients with chronic periodontitis pre and post non-surgical periodontal therapy.
Aim: The aim of this study was to evaluate the micronutrient levels in saliva in patients with chronic periodontitis pre and post non-surgical periodontal therapy.
Materials and Method: The study comprised of 30 patients divided into 2 groups. Group 1 consisted of 15 patients with chronic periodontitis and group 2 consisted of 15 healthy individuals. Phase 1 therapy was done for all subjects. Micronutrient levels (zinc, magnesium and copper) and clinical parameters such as gingival index, pocket depth and clinical attachment loss were evaluated at baseline and 1 month after periodontal therapy.
Results: The results of the study showed that on comparing baseline parameters between the test and control group, there is a marked decrease in micronutrient level of Zinc in the test group when compared with the control group and a marked increase in micronutrient level of Copper in the test group when compared with the control group. On comparing the effect of non-surgical therapy on micronutrient levels in the test group, our results showed improvement in the micronutrient levels. Compared with baseline, the levels of copper showed a decrease after treatment and the levels of zinc showed an increase in response to treatment.