One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Md. Saikat Islam Khan, Anichur Rahman, Tanoy Debnath, Md. Razaul Karim, Mostofa Kamal Nasir, Shahab S. Band, Amir Mosavi, Abdollah Dehzangi
Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. This paper proposes two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma, and pituitary) brain tumors. We use two publicly available datasets that include 3064 and 152 MRI images, respectively. To build our models, we first apply a 23-layers convolution neural network (CNN) to the first dataset since there is a large number of MRI images for the training purpose. However, when dealing with limited volumes of data, which is the case in the second dataset, our proposed "23-layers CNN" architecture faces overfitting problem. To address this issue, we use transfer learning and combine VGG16 architecture along with the reflection of our proposed "23 layers CNN" architecture. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our models achieve up to 97.8% and 100% classification accuracy for our employed datasets, respectively, exceeding all other state-of-the-art models. Our proposed models, employed datasets, and all the source codes are publicly available at: (https://github.com/saikat15010/Brain-Tumor-Detection).
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., Mosavi, A., & Dehzangi, A. (2022). Accurate brain tumor detection using deep convolutional neural network. Computational and Structural Biotechnology Journal, 20, 4733–4745. https://doi.org/10.1016/j.csbj.2022.08.039
Khan, Md. Saikat Islam, et al. "Accurate brain tumor detection using deep convolutional neural network." Computational and Structural Biotechnology Journal, vol. 20, 2022, pp. 4733–4745, https://doi.org/10.1016/j.csbj.2022.08.039.
Khan, Md. Saikat Islam, Anichur Rahman, Tanoy Debnath, Md. Razaul Karim, Mostofa Kamal Nasir, Shahab S. Band, Amir Mosavi, and Abdollah Dehzangi. "Accurate brain tumor detection using deep convolutional neural network." Computational and Structural Biotechnology Journal 20 (2022): 4733–4745. https://doi.org/10.1016/j.csbj.2022.08.039.
Khan M. S. I., Rahman A., Debnath T. [и др.] Accurate brain tumor detection using deep convolutional neural network // Computational and Structural Biotechnology Journal. — 2022. — Vol. 20. — P. 4733–4745. — URL: https://doi.org/10.1016/j.csbj.2022.08.039
@article{khan2022accurate,
title = {{Accurate brain tumor detection using deep convolutional neural network}},
author = {Md. Saikat Islam Khan and Anichur Rahman and Tanoy Debnath and Md. Razaul Karim and Mostofa Kamal Nasir and Shahab S. Band and Amir Mosavi and Abdollah Dehzangi},
journal = {Computational and Structural Biotechnology Journal},
year = {2022},
volume = {20},
pages = {4733--4745},
doi = {10.1016/j.csbj.2022.08.039},
url = {https://doi.org/10.1016/j.csbj.2022.08.039}
}TY - JOUR AU - Khan, Md. Saikat Islam AU - Rahman, Anichur AU - Debnath, Tanoy AU - Karim, Md. Razaul AU - Nasir, Mostofa Kamal AU - Band, Shahab S. AU - Mosavi, Amir AU - Dehzangi, Abdollah TI - Accurate brain tumor detection using deep convolutional neural network JO - Computational and Structural Biotechnology Journal VL - 20 SP - 4733 EP - 4745 PY - 2022 DA - 2022/01/01 DO - 10.1016/j.csbj.2022.08.039 UR - https://doi.org/10.1016/j.csbj.2022.08.039 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.