Aplicação do modelo Faster R-CNN para detecção e classificação de tumores cerebrais primários em imagens de ressonância magnética

ABDULKAREEM, Sulyman Age; ABDULKAREEM, Zainab Olorunbukademi. An evaluation of the Wisconsin breast cancer dataset using ensemble classifiers and RFE feature selection technique. Internacional Journal of Sciences: Basic Applied Research, Amman, v. 55, n. 2, p. 67-80, 2021. Disponível em: https://www.gssrr.org/JournalOfBasicAndApplied/article/view/12300. Acesso em: 5 fev. 2026. AKTER, Atika et al. Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Systems with Applications, [s. l.], v. 238, parte F, p. 122347, mar. 2024. DOI: https://doi.org/10.1016/j.eswa.2023.122347. Disponível em: https://www.sciencedirect.com/science/article/pii/S095741742302849X. Acesso em: 5 fev. 2026. ANANTHARAJAN, Shenbagarajan et al. MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors, London, v. 31, p. 101026, feb. 2024. DOI: https://doi.org/10.1016/j.measen.2024.101026. Disponível em: https://www.sciencedirect.com/science/article/pii/S2665917424000023?via%3Dihub. Acesso em: 5 fev. 2026. ASHTAGI, Rashmi et al. Image fusion of MRI and CT scan for brain tumor detection using VGG-19. International Journal of Intelligent Systems and Applications in Engineering, [s. l.], v. 12, n. 10s, p. 369-377, 2024. Disponível em: https://ijisae.org/index.php/IJISAE/article/view/4386. Acesso em: 5 fev. 2026. BAIRAGI, Vinayak K. et al. Automatic brain tumor detection using CNN transfer learning approach. Medical & Biological Engineering & Computing, New York, v. 61, n. 7, p. 1821-1836, 2023. DOI: https://doi.org/10.1007/s11517-023-02820-3. Disponível em: https://link.springer.com/article/10.1007/s11517-023-02820-3. Acesso em: 5 fev. 2026. BANERJEE, Kunal et al. Exploring alternatives to softmax function. arXiv, Ithaca, 20 nov. 2020. Preprint. DOI: https://doi.org/10.48550/arXiv.2011.11538. Disponível em: https://arxiv.org/abs/2011.11538. Acesso em: 5 fev. 2026. CHANU, Maibam Mangalleibi et al. RETRACTED ARTICLE: Computer-aided detection and classification of brain tumor using YOLOv3 and deep learning. Soft Computing, [s. l.], v. 27, p. 9927-9940, 2023. DOI: https://doi.org/10.1007/s00500-023-08343-1. Disponível em: https://link.springer.com/article/10.1007/s00500-023-08343-1. Acesso em: 5 fev. 2026. CHEN, Shih-Lun et al. Detection of various dental conditions on dental panoramic radiography using Faster R-CNN. IEEE Access, [s. l.], v. 11, p. 127388-127401, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3332269. Disponível em: https://ieeexplore.ieee.org/document/10315113. Acesso em: 5 fev. 2026. DEMIRTAS, Asiye; ERDEMIR, Gökhan; BAYRAM, Haluk. Indoor surface classification for mobile robots. PeerJ Computer Science, London, v. 10, p. e1730, 2024. DOI: https://doi.org/10.7717/peerj-cs.1730. Disponível em: https://peerj.com/articles/cs-1730/. Acesso em: 5 fev. 2026. FAROOQ, Muhammad Shoaib; TARIQ, Ayesha. A deep learning architectures for kidney disease classification. arXiv, Ithaca, 23 mar. 2024. Preprint. DOI: https://doi.org/10.48550/arXiv.2403.15895. Disponível em: https://arxiv.org/abs/2403.15895. Acesso em: 5 fev. 2026. GULHANE, Monali et al. Development of a temporal analysis model augmented for disease progression identification through multiparametric analysis. International Journal of Intelligent Systems and Applications in Engineering, [s. l.], v. 12, n. 2, p. 620-634, 2024. Disponível em: https://ijisae.org/index.php/IJISAE/article/view/4305. Acesso em: 5 fev. 2026. GUPTA, Brij B.; GAURAV, Akshat; ARYA, Varsha. Deep CNN based brain tumor detection in intelligent systems. International Journal of Intelligent Networks, [s. l.], v. 5, p. 30-37, 2024. DOI: https://doi.org/10.1016/j.ijin.2023.12.001. Disponível em: https://www.sciencedirect.com/science/article/pii/S2666603023000465?via%3Dihub. Acesso em: 5 fev. 2026. HAFEEZ, Hafiz Aamir et al. A CNN-model to classify low-grade and high-grade glioma from MRI images. IEEE Access, [s. l.], v. 11, p. 46283-46296, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3273487. Disponível em: https://ieeexplore.ieee.org/document/10119150. Acesso em: 5 fev. 2026. HASHEMI, Seyed Mohammed Hossein. Crystal clean: brain tumors MRI dataset. [S. l.]: Kaggle, 2023. Data set. Disponível em: https://www.kaggle.com/datasets/mohammadhossein77/brain-tumors-dataset. Acesso em: 9 maio 2023. HASHEMZEHI, Raheleh et al. Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE. Biocybernetics and Biomedical Engineering, [s. l.], v. 40, n. 3, p. 1225-1232, jul. Sept. 2020. DOI: https://doi.org/10.1016/j.bbe.2020.06.001. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0208521620300784?via%3Dihub. Acesso em: 5 fev. 2026. KIANI KALEJAHI, Behnam K.; MESHGINI, Saeed; DANISHVAR, Sebelan. Brain tumor segmentation by auxiliary classifier generative adversarial network. Signal, Image and Video Processing, [s. l.], v. 17, p. 3339-3345, 2023. DOI: https://doi.org/10.1007/s11760-023-02555-6. Disponível em: https://link.springer.com/article/10.1007/s11760-023-02555-6. Acesso em: 5 fev. 2026. KARAALTUN, Muhammed. Whole image average pooling-based convolution neural network approach for brain tumour classification. Neural Computing and Applications, [s. l.], v. 36, p. 1351-1367, 2024. DOI: https://doi.org/10.1007/s00521-023-09108-5. Disponível em: https://link.springer.com/article/10.1007/s00521-023-09108-5. Acesso em: 5 fev. 2026. KAYA, Ömer; ÇODUR, Muhammed Yasin; MUSTAFARAJ, Enea. Automatic detection of pedestrian crosswalk with Faster R-CNN and YOLOv7. Buildings, [s. l.], v. 13, n. 4, p. 1070, 2023. DOI: https://doi.org/10.3390/buildings13041070. Disponível em: https://www.mdpi.com/2075-5309/13/4/1070. Acesso em: 5 fev. 2026. KHAN, Siraj et al. A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. IEEE Access, [s. l.], v. 9, p. 10657-10673, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3048172. Disponível em: https://ieeexplore.ieee.org/document/9311202. Acesso em: 5 fev. 2026. KUMAR, Ajay; MA, Yan. Localization and classification of brain tumor using multi-layer perceptron. In: ACHARJYA, D. P.; MA, Kun (ed). Computational intelligence in healthcare informatics. Singapore: Springer Nature Singapore, 2024. p. 93-103. (Studies in Computational Intelligence, 1132). DOI: https://doi.org/10.1007/978-981-99-8853-2_6. Disponível em: https://link.springer.com/chapter/10.1007/978-981-99-8853-2_6. Acesso em: 15 jan. 2025. KUMAR, Sunil; KUMAR, Dilip. Human brain tumor classification and segmentation using CNN. Multimedia Tools and Applications, v. 82, n. 5, p. 7599-7620, 2023. DOI: https://doi.org/10.1007/s11042-022-13713-2. Disponível em: https://dl.acm.org/doi/10.1007/s11042-022-13713-2. Acesso em: 5 fev. 2026. LOUIS, David N. et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica, Berlin, v. 131, n. 6, p. 803-820, 2016. DOI: https://doi.org/10.1007/s00401-016-1545-1. Disponível em: https://link.springer.com/article/10.1007/s00401-016-1545-1. Acesso em: 5 fev. 2026. MAJIB, Mohammad Shahjahan et al. VGG-SCNet: a VGG Net based deep learning framework for brain tumor detection on MRI images. IEEE Access, [s. l.], v. 9, p. 116942-116952, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3105874. Disponível em: https://ieeexplore.ieee.org/document/9515947. Acesso em: 5 fev. 2026. MEHRA, Sumiran et al. An empirical evaluation of enhanced performance softmax function in deep learning. IEEE Access, [s. l.], v. 11, p. 34912-34924, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3265327. Disponível em: https://ieeexplore.ieee.org/document/10093848. Acesso em: 5 fev. 2026. METHA, Halifa Sekar; KUSRINI, Kusrini; ARIATMANTO, Dhani. Classification of types Roasted Coffee Beans using Convolutional Neural Network Method. SinkrOn, North Sumatra, v. 8, n. 2, p. 846-851, 2024. DOI: https://doi.org/10.33395/sinkron.v8i2.13517. Disponível em: https://jurnal.polgan.ac.id/index.php/sinkron/article/view/13517. Acesso em: 5 fev. 2026. NGUYEN, Chi Cuong et al. Pulmonary nodule detection based on Faster R-CNN with adaptive anchor box. IEEE Access, [s. l.], v. 9, p. 154740-154751, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3128942. Disponível em: https://ieeexplore.ieee.org/document/9619980. Acesso em: 5 fev. 2026. NOGUEIRA, Tiago do Carmo et al. Reference-based model using multimodal gated recurrent units for image captioning. Multimedia Tools and Applications, [s. l.], v. 79, p. 30615-30635, 2020. DOI: https://doi.org/10.1007/s11042-020-09539-5. Disponível em: https://link.springer.com/article/10.1007/s11042-020-09539-5. Acesso em: 5 fev. 2026. PANDA, Shreya et al. Faster R-CNN decoded: Elevating object detection with precision and speed. International Research Journal of Modernization in Engineering Technology and Science, Indore, v. 6, n. 1, p. 1720-1726, jan. 2024. DOI: https://www.doi.org/10.56726/IRJMETS48464. Disponível em: https://www.irjmets.com/uploadedfiles/paper//issue_1_january_2024/48464/final/fin_irjmets1705430132.pdf. Acesso em: 15 jan. 2025. PRAKASH, B. V. et al. Meningioma brain tumor detection and classification using hybrid CNN method and Ridgelet Transform. Scientific Reports, London, v. 13, p. 14522, 2023. DOI: https://doi.org/10.1038/s41598-023-41576-6. Disponível em: https://www.nature.com/articles/s41598-023-41576-6. Acesso em: 5 fev. 2026. RAHMAN, Md Atiqur; WANG, Yang. Optimizing intersection-over-union in deep neural networks for image segmentation. In: BEBIS, George et al. (ed.) Advances in Visual Computing: International Symposium on Visual Computing. Cham: Springer, 2016. p. 234-244. (Lecture Notes in Computer Science, 10072). DOI: https://doi.org/10.1007/978-3-319-50835-1_22. Disponível em: https://link.springer.com/chapter/10.1007/978-3-319-50835-1_22. Acesso em: 5 fev. 2026. RAJENDRAN, Surendran et al. Automated segmentation of brain tumor MRI images using deep learning. IEEE Access, [s. l.], v. 11, p. 64758-64768, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3288017. Disponível em: https://ieeexplore.ieee.org/document/10156786. Acesso em: 5 fev. 2026. RANA, Meghavi; BHUSHAN, Megha. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimedia Tools and Applications, Hingham, v. 82, p. 26731-26769, 2023. DOI: https://doi.org/10.1007/s11042-022-14305-w. Disponível em: https://link.springer.com/article/10.1007/s11042-022-14305-w. Acesso em: 5 fev. 2026. REN, Shaoqing et al. Faster R-CNN: towards real-time object detection with Region Proposal Networks. arXiv, Ithaca, 4 jun. 2015. Preprint. DOI: https://doi.org/10.48550/arXiv.1506.01497. Disponível em: https://arxiv.org/abs/1506.01497. Acesso em: 5 fev. 2025. RIZWAN, Muhammad et al. Brain tumor and glioma grade classification using Gaussian Convolutional Neural Network. IEEE Access, [s. l.], v. 10, p. 29731-29740, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3153108. Disponível em: https://ieeexplore.ieee.org/document/9718069. Acesso em: 5 fev. 2026. SAEEDI, Soheila et al. MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Medical Informatics and Decision Making, London, v. 23, n. 1, p. 16, 2023. DOI: https://doi.org/10.1186/s12911-023-02114-6. Disponível em: https://link.springer.com/article/10.1186/s12911-023-02114-6. Acesso em: 5 fev. 2026. SIMO, Alain Marcel Dikande et al. Introducing a deep learning method for brain tumor classification using MRI data towards better performance. Informatics in Medicine Unlocked, [s. l.], v. 44, p. 101423, 2024. DOI: https://doi.org/10.1016/j.imu.2023.101423. Disponível em: https://www.sciencedirect.com/science/article/pii/S2352914823002691. Acesso em: 5 fev. 2026. SONGPENGCHENG, Songpengcheng. Research on weak and small target detection technology in infrared scene. SSRN, Amsterdan, 22 ago. 2023. Preprint. DOI: https://dx.doi.org/10.2139/ssrn.4548818. Disponível em: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4548818. Acesso em: 5 fev. 2026. TONG, Zanjia et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv, Ithaca, 24 jan. 2023. Preprint. DOI: https://doi.org/10.48550/arXiv.2301.10051. Disponível em: https://arxiv.org/abs/2301.10051. Acesso em: 5 fev. 2026. TÜRK, Fuat; KÖKVER, Yunus. Detection of lung opacity and treatment planning with three-channel fusion CNN model. Arabian Journal for Science and Engineering, Berlin, v. 49, n. 3, p. 2973-2985, 2024. DOI: https://doi.org/10.1007/s13369-023-07843-4. Disponível em: https://link.springer.com/article/10.1007/s13369-023-07843-4. Acesso em: 5 fev. 2026. VANKDOTHU, Ramdas; HAMEED, Mohd Abdul. Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning. Measurement: Sensors, London, v. 24, p. 100440, dez. 2022. DOI: https://doi.org/10.1016/j.measen.2022.100440. Disponível em: https://www.sciencedirect.com/science/article/pii/S2665917422000745. Acesso em: 5 fev. 2026. VIDYARTHI, Ankit et al. Machine learning assisted methodology for multiclass classification of malignant brain tumors. IEEE Access, [s. l.], v. 10, p. 50624-50640, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3172303. Disponível em: https://ieeexplore.ieee.org/document/9772610. Acesso em: 5 fev. 2026. WANG, Jian et al. RanMerFormer: randomized vision transformer with token merging for brain tumor classification. Neurocomputing, [s. l.], v. 573, p. 127216, mar. 2024. DOI: https://doi.org/10.1016/j.neucom.2023.127216. Disponível em: https://www.sciencedirect.com/science/article/pii/S0925231223013395. Acesso em: 5 fev. 2026. YI, Zeren et al. Detection and localization for lake floating objects based on CA-Faster R-CNN. Multimedia Tools and Applications, [s. l.], v. 81, n. 12, p. 17263-17281, 2022. Disponível em: https://link.springer.com/article/10.1007/s11042-022-12686-6. Acesso em: 5 fev. 2026. YOUNIS, Ayesha et al. Deep learning techniques for the classification of brain tumor: a comprehensive survey. IEEE Access, [s. l.], v. 11, p. 113050-113063, Sept. 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3317796. Disponível em: https://ieeexplore.ieee.org/document/10256178. Acesso em: 5 fev. 2026. ZHANG, Ethan; ZHANG, Yi. Average precision. In: LIU, Ling; ÖZSU, M. Tamer (ed). Encyclopedia of database systems. Boston: Springer, 2009. p. 192-193. DOI: https://doi.org/10.1007/978-0-387-39940-9_482. Disponível em: https://link.springer.com/rwe/10.1007/978-0-387-39940-9_482. Acesso em: 5 fev. 2026.