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All Journal JURNAL ELEKTRO EMITTER International Journal of Engineering Technology
Rokhmat Febrianto
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Deep Learning-Based Brain Tumor Classification Using Convolutional Neural Network Sasita, Naumi; Futri Zalzabilah Ray; M.Fauzanil Wildan A.R.; Tantowi Hutagalung; Rokhmat Febrianto
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6658

Abstract

An essential noninvasive medical diagnostic technique is magnetic resonance imaging (MRI), which is particularly useful for identifying brain cancers. While earlier algorithms proved effective on smaller MRI datasets, their performance suffered on bigger datasets. This study addresses the need for a swift and reliable brain tumor classification system capable of sustaining optimal performance across comprehensive MRI datasets. The convolutional neural network is implemented using the Keras library, incorporating the ResNet50 architecture as a pre-trained model. The ResNet50 model is fine-tuned for the specific brain tumor classification task, with a Global Average Pooling layer, dropout, and a final dense layer with softmax activation. Data augmentation techniques are employed to enhance the model’s robustness, including rotation, width and height shifts, and horizontal flips. The training process involves optimizing the model using the Adam optimizer with a learning rate of 0.0001. Early stopping, learning rate reduction on plateau, and model checkpointing are implemented as callbacks to ensure efficient training and prevent overfitting. The proposed model achieves a remarkable accuracy of 99.28 percent after 15 epochs. The classification task involves distinguishing among four classes: glioma, meningioma, pituitary, and no tumor.
Automatic Detection of Wrecked Airplanes from UAV Images Risnumawan, Anhar; Perdana, Muhammad Ilham; Alif Habib Hidayatulloh; A. Khoirul Rizal; Indra Adji Sulistijono; Achmad Basuki; Rokhmat Febrianto
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.424

Abstract

Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.
Co-Authors A. Khoirul Rizal Achmad Basuki Alif Habib Hidayatulloh Futri Zalzabilah Ray Indra Adji Sulistijono M.Fauzanil Wildan A.R. Perdana, Muhammad Ilham Risnumawan, Anhar Sasita, Naumi Tantowi Hutagalung