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All Journal Bulletin of Electrical Engineering and Informatics Journal of Environmental Science and Sustainable Development
Islam, Rakibul
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Humanities Electrical & Electronics Engineering Energy Environmental Science Social Sciences

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A fine tune robust transfer learning based approach for brain tumor detection using VGG-16 Islam, Rakibul; Akhi, Amatul Bushra; Akter, Farzana
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5646

Abstract

Brain tumor recognition by magnetic resonance imaging (MRI) is crucial because it improves survival rates and allows them to plan treatments accordingly. An accumulation of abnormal cells known as a brain tumor can spread to nearby tissues and endanger the patient. Magnetic resonance imagery is the primary imaging technique which determines the extent of brain tumors. Deep learning techniques rapidly grew in computer vision due to ample data for model training and improved designs on applications. MRI has shown promising results when using deep learning approaches to identify and classify brain tumors. This study uses MRI data and a convolutional neural network (CNN) to create a reliable transfer learning model that classifies tumors under four classes. Brain tumors' unwanted parts are excised, the quality is improved, and the cancer is coloured. By eliminating artefacts, decreasing noise, and boosting the image. The number of MRI images has increased using two augmentation techniques. A number of CNN architectures, including VGG19, VGG16, MobileNet, InceptionV3, and MobileNetV2 analyzed the augmented dataset. Where VGG-16 provides the accuracy of highest level. The best model underwent a hyperparameter ablation investigation, which led to the suggested hyper-tuned VGG16 obtaining 99.21% test and validation accuracy and 99.01% test accuracy.
CATALYZING METEOROLOGICAL INSIGHTS WITH A COST-EFFECTIVE WEATHER MONITORING SYSTEM Alam, Ariful; Muntaha, Sidratula; Munshi, Poonam; Khan, Israt; Islam, Rakibul; Bhuian, Jamil; Arafat, Md.Yasir; Hasan, Md. Ridwanul
Journal of Environmental Science and Sustainable Development Vol. 7, No. 2
Publisher : UI Scholars Hub

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Abstract

Undoubtedly, one of the biggest alarming phenomena of this decade is the tremendous fluctuations in the weather and climate. Therefore, different types of surveys, investigations, and research are required in this regard in every region. A low-cost weather monitoring system can be implemented in every educational and research institute to collect and analyze different types of weather-related data. This study establishes the method of developing such a system and analyzing data in a simplified way which the data gathered during thunderstorms and cyclonic activity in Bangladesh. The system was designed with Proteus 8 professional software and developed by using a microcontroller, a temperature-humidity sensor, a wind speed analyzer, an automated rainfall analyzer, a barometric pressure sensor, and an LDR-based lightning bolt analyzer with a Linux-operated computer. The result obtained from the developed system is calibrated and compared with the standard value or theoretical value. The comparison graph shows that the developed system is efficient and reliable. After calibrating the system, several data points were collected at Mawlana Bhashani Science and Technology University, Tangail, Bangladesh. Developing an in-house weather monitoring system allows institutions to avoid costly foreign data purchases, reducing expenses and reliance on international services. Practically, this research can be applied to support climate studies and localized forecasting without the expense of high foreign exchange rates, allowing for more affordable meteorological research and enhancing local expertise.
Co-Authors Akhi, Amatul Bushra Akter, Farzana Alam, Ariful Arafat, Md.Yasir Bhuian, Jamil Hasan, Md. Ridwanul Khan, Israt Munshi, Poonam Muntaha, Sidratula