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Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

Minimally invasive surgery offers several advantages, including reduced blood loss, smaller incisions, less pain, and a lower risk of complications than open surgery. This approach enhances patient comfort and supports faster recovery. When guided by optimal path planning, surgical robots can accurately navigate the body to remove malignant tumors with high precision. This study proposes a Modified Particle Swarm Optimization (MPSO) algorithm to determine the optimal path for robotic-assisted minimally invasive surgery targeting brain tumors. The algorithm improves upon standard PSO by modifying the velocity update equation and incorporating an adaptive inertia weight, enhancing convergence speed, global search ability, and solution accuracy. Experimental results show that the proposed MPSO achieves a maximum fitness value of 19.10 in a sparse obstacle environment, outperforming standard PSO and IPSO in quality and in the required number of iterations. The approach effectively balances path efficiency and obstacle avoidance, making it well-suited for complex surgical scenarios. In conclusion, the MPSO-based method provides a reliable and precise solution for robotic surgical navigation, improving outcomes and safety in minimally invasive procedures.

The development of computer technology helps many aspects of life. One aspect of life that takes advantage of technological developments is the health sector, in order to solve problems including brain tumors. Brain Tumor Disease is the growth of abnormal cells in or around the brain in an unnatural and uncontrolled manner. Patients with brain tumors continue to increase every year, because the initial symptoms are often underestimated. Therefore created a software that can help diagnose brain tumors using the certainty factor method

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.

This research aims to develop an expert system that can diagnose diseases related to brain tumors using the Case Based Reasoning (CBR) method. The CBR method works by comparing new cases with previous cases that have been stored in the database to provide appropriate diagnoses and treatment recommendations. This system is designed to assist medical personnel in analyzing patient symptoms, thus speeding up the process of identifying the type of brain tumor. In addition, the system is also equipped with a knowledge base obtained from real cases that have been validated by medical experts. Test results show that the diagnostic accuracy of this system reaches a fairly high level, especially in detecting frequently encountered types of brain tumors. Thus, this system has the potential to be an effective tool in the medical diagnosis process, especially in patients who show symptoms of brain tumors.

Procedure For Magnetic Resonance Spectroscopy Examination Technique In Brain Tumor Cases At The Radiology Installation Of Pertamina Central Hospital Jakarta. MR spectroscopy is one way of describing metabolites in tumor tissue without being invasive. Single spectroscopic approach, which is used in Magnetic Resonance Spectroscopy and is the most common spectral collection method in MRI modalities. MRi is the establishment of a medical diagnosis based on the principle of magnetic resonance. MRI aircraft produce images in various pieces namely coronal, sagittal, and transverse by not at all using x-rays that produce radiation and also using radioactive substances. Brain tumors are abnormally growing brain tissue cells originating from the brain or meningens, which are benign or malignant tumors that make massive pressure. MRS is a unique non-invasive sequence that helps in the identification of lesion molecules and the difference between malignant and benign lesions. In MRI examination of the brain with tumor cases there are several sequences used, namely Se / Fse Pd / T2, Se / Fse / incoherent spoiled, (DWI) diffusion weighted imaging (1) According to Elmaoglu (2), imaging techniques that can be used for MRI brain tumor cases include axial T1 spin echo, T2 fast recovery fast spin echo, T2 FLAIR, coronal T1 and T1 Axial post contrast injection,  T1 coronal, and T1 sagittal, diffusion weighted imaging (DWI), magnetic resonance angiography, and magnetic resonance spectroscopy. In taking SVS LESION localizer must be precise on tumor tissue not taking part that is not part of the tumor, analyze tumor area and take SVS NORMAL localizer on other healthy tissue.

Comparison Of Metabolic Value On MR-Spectroscopy With and Without Contrast Media at Persahabatan Hospital, East Jakarta. Magnetic Resonance Spectroscopy is a non-invasive technique that can be used to measure the metabolism of several biochemical components in body tissue, especially the brain. Based on observations made by the author at Persahabatan Hospital, the spectroscopy technique used at Persahabatan Hospital is a multi-voxle technique, with sequences selction namely s2D_PRESS_144, where the spectroscopy images are taken after administering contrast and prevalence for tumor cases in the Radiology Installation at Persahabatan Hospital, with 5 data from the month May-August 2023. To determine the validity of this opinion, the authors performed pre and post- contrast MR-Spectroscopy on patients with contrast and compared the results with patients without contrast. In this study the author used a quantitative analysis type of research with an experimental approach aimed at whether the administration of contrast material affects the results of MR-Spectroscopy in patients with brain tumor cases. Where the research data comes from primary data in the Radiology installation at Persahabatan Hospital from May - August 2023 using 5 patient data. Results: Based on an observational study carried out on primary data from 5 patients, there were differences in metabolic values with and without contrast media in brain tumor cases. And MR-Spectroscopy examination without contrast is better to use than with contrast, although from the overall data it turns out there are some data that say post- contrast is higher.

ABSTRAK The development of computer technology helps many aspects of life. One aspect of life that takes advantage of technological developments is the health sector, in order to solve problems including brain tumors. Brain Tumor Disease is the growth of abnormal cells in or around the brain in an unnatural and uncontrolled manner. Patients with brain tumors continue to increase every year, because the initial symptoms are often underestimated. Therefore created a software that can help diagnose brain tumors using the certainty factor method