MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture

  • Novia Adelia Ujilast Universitas Muhammadiyah Malang
  • Nuris Sabila Firdausita Universitas Muhammadiyah Malang
  • Christian Sri Kusuma Aditya Universitas Muhammadiyah Malang
  • Yufis Azhar Universitas Muhammadiyah Malang
Keywords: alzheimer's disease, convolutional neural network, efficientnet-B0, efficientnet-B3


Alzheimer's disease is a neurodegenerative disorder or a condition characterized by degeneration and damage to the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-based recognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes using the convolutional neural network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study used 6400 images that encompass four classes, namely mild demented, moderate demented, non-demented, and very mild demented. After conducting testing for both scenarios, the exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the exactness was 97.00%.


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S. K, L. S.K., A. Khanna, S. Tanwar, J. J. P. C. Rodrigues, and N. R. Roy, “Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier,” Computers & Electrical Engineering, vol. 77, pp. 230–243, Jul. 2019, doi: 10.1016/j.compeleceng.2019.06.001.

J. Li, B. Maharjan, B. Xie, and C. Tao, “A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation,” J Med Internet Res, vol. 22, no. 9, p. e19897, Sep. 2020, doi: 10.2196/19897.

R. Mutiara Gemiralda and M. Marlaokta, “Efek Neuroprotektor Kunyit pada Pasien Alzheimer,” 2019.

A. G. M. Sianturi, “Stadium, Diagnosis, dan Tatalaksana Penyakit Alzheimer,” Majalah Kesehatan Indonesia, vol. 2, no. 2, pp. 39–44, Oct. 2021, doi: 10.47679/makein.202132.

C. Reitz, E. Rogaeva, and G. W. Beecham, “Late-onset vs nonmendelian early-onset Alzheimer disease,” Neurol Genet, vol. 6, no. 5, p. e512, Oct. 2020, doi: 10.1212/NXG.0000000000000512.

J. Neelaveni and M. S. G. Devasana, “Alzheimer Disease Prediction using Machine Learning Algorithms,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2020, pp. 101–104. doi: 10.1109/ICACCS48705.2020.9074248.

N. Almumtazah, M. S. Kiromi, N. Ulinnuha, and P. Korespondensi, “Klasifikasi Alzheimer berdasarkan Data Citra MRI Otak menggunakan FCM dan ANFIS Alzheimer Classification based on Brain MRI Images using FCM and ANFIS,” vol. 10, no. 3, pp. 613–622, 2023, doi: 10.25126/jtiik.2023106826.

B. Huang, F. Yang, M. Yin, X. Mo, and C. Zhong, “A Review of Multimodal Medical Image Fusion Techniques,” Comput Math Methods Med, vol. 2020, pp. 1–16, Apr. 2020, doi: 10.1155/2020/8279342.

Y. Zhu and X. Zhu, “MRI-Driven PET Image Optimization for Neurological Applications,” Front Neurosci, vol. 13, Jul. 2019, doi: 10.3389/fnins.2019.00782.

A. W. Salehi, P. Baglat, B. B. Sharma, G. Gupta, and A. Upadhya, “A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI,” in 2020 International Conference on Smart Electronics and Communication (ICOSEC), IEEE, Sep. 2020, pp. 156–161. doi: 10.1109/ICOSEC49089.2020.9215402.

U. N. Oktaviana, R. Hendrawan, A. D. K. Annas, and G. W. Wicaksono, “Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 6, pp. 1216–1222, Dec. 2021, doi: 10.29207/resti.v5i6.3607.

I. N. Purnama, “Herbal Plant Detection Based on Leaves Image Using Convolutional Neural Network With Mobile Net Architecture,” JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), vol. 6, no. 1, pp. 27–32, 2020.

S. Sharan, H. Harsh, S. Kininmonth, and U. Mehta, “Automated cnn based coral reef classification using image augmentation and deep learning,” International Journal of Engineering Intelligent Systems, vol. 29, no. 4, pp. 253–261, 2021.

M. N. Ichsan, N. Armita, A. E. Minarno, F. D. S. Sumadi, and Hariyady, “Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 4, pp. 606–611, Aug. 2022, doi: 10.29207/resti.v6i4.4222.

T. Bayu Sasongko and A. Amrullah, “Analisis Efek Augmentasi Dataset pada Algoritma Pre-Trained Convolutional Neural Network(CNN),” vol. 10, no. 4, pp. 763–768, 2023, doi: 10.25126/jtiik.2023106583.

M. Junihardi, S. Sanjaya, L. Handayani, and F. Syafria, “Klasifikasi Daging Sapi dan Daging Babi Menggunakan Arsitektur EfficientNet-B3 dan Augmentasi Data,” Jurnal TEKINKOM, vol. 6, no. 1, 2023, doi: 10.37600/tekinkom.v6i1.845.

Y. Miftahuddin and F. Zaelani, “Perbandingan Metode Efficientnet-B3 dan Mobilenet-V2 Untuk Identifikasi Jenis Buah-buahan Menggunakan Fitur Daun,” 2022.

M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” May 2019, [Online]. Available:

H. A. Shah, F. Saeed, S. Yun, J. H. Park, A. Paul, and J. M. Kang, “A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet,” IEEE Access, vol. 10, pp. 65426–65438, 2022, doi: 10.1109/ACCESS.2022.3184113.

A. Priyatama, Z. Sari, and Y. Azhar, “Deep Learning Implementation using Convolutional Neural Network for Alzheimer’s Classification,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 2, pp. 310–217, Mar. 2023, doi: 10.29207/resti.v7i2.4707.

R. Singh, N. Sharma, and R. Gupta, “Detection of Alzheimer’s Risk Level using Inception V3 Transfer Learning Model,” in 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE, Apr. 2023, pp. 1–6. doi: 10.1109/ICDCECE57866.2023.10151235.

D. Ganesh, M. S. Kumar, C. Aparna, C. J. Royal, D. Vinay, and S. H. Sari, “Implementation Of Convolutional Neural Networks For Detection Of Alzheimer’s Disease,” Journal for New Zealand Herpetology, vol. 12, no. 1, 2023.

Y. Selim Taspinar and Y. Selim TASPINAR, Classification of Alzheimer MRI Images with Machine Learning Methods Using Deep Features. [Online]. Available:

D. F. Santos, “Advancing Automated Diagnosis: Convolutional Neural Advancing Automated Diagnosis: Convolutional Neural Networks for Alzheimer’s Disease Classification through MRI Networks for Alzheimer’s Disease Classification through MRI Image Processing Image Processing”, doi: 10.36227/techrxiv.23002007.v1.

A. D. Huri, R. A. Suseno, and Y. Azhar, “Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 6, pp. 952–957, Dec. 2022, doi: 10.29207/resti.v6i6.4357.

H. Alhichri, A. S. Alswayed, Y. Bazi, N. Ammour, and N. A. Alajlan, “Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model with Attention,” IEEE Access, vol. 9, pp. 14078–14094, 2021, doi: 10.1109/ACCESS.2021.3051085.

W. R. Perdani, R. Magdalena, And N. K. Caecar Pratiwi, “Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, no. 2, p. 322, Apr. 2022, doi: 10.26760/elkomika.v10i2.322.

B. D. Mardiana, W. B. Utomo, U. N. Oktaviana, G. W. Wicaksono, and A. E. Minarno, “Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 20–26, Feb. 2023, doi: 10.29207/resti.v7i1.4550.

L. Mutawalli, M. T. A. Zaen, and W. Bagye, “Klasifikasi Teks Sosial Media Twitter Menggunakan Support Vector Machine (Studi Kasus Penusukan Wiranto),” Jurnal Informatika dan Rekayasa Elektronik, vol. 2, no. 2, p. 43, Dec. 2019, doi: 10.36595/jire.v2i2.117.

B. P. Pratiwi, A. S. Handayani, and S. Sarjana, “Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi Wsn Menggunakan Confusion Matrix,” Jurnal Informatika Upgris, vol. 6, no. 2, Jan. 2021, doi: 10.26877/jiu.v6i2.6552.

C. Meckbach, V. Tiesmeyer, and I. Traulsen, “A promising approach towards precise animal weight monitoring using convolutional neural networks,” Comput Electron Agric, vol. 183, p. 106056, Apr. 2021, doi: 10.1016/j.compag.2021.106056.

A. Agarwal, S. Vats, R. Agarwal, A. Ratra, V. Sharma, and A. Jain, “Efficient NetB3 for Automated Pest Detection in Agriculture,” in 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2023, pp. 1408–1413.

L. T. Duong, P. T. Nguyen, C. Di Sipio, and D. Di Ruscio, “Automated fruit recognition using EfficientNet and MixNet,” Comput Electron Agric, vol. 171, p. 105326, Apr. 2020, doi: 10.1016/j.compag.2020.105326.

How to Cite
Ujilast, N. A., Firdausita, N. S., Aditya, C. S. K., & Azhar, Y. (2024). MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 18 - 25.
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