Treffer: Enhanced Brain Tumor Classification with Convolutional Neural Networks.
Title:
Enhanced Brain Tumor Classification with Convolutional Neural Networks.
Authors:
Kanavos A; Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece. icsdd20017@icsd.aegean.gr., Papadimitriou O; Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece., Vonitsanos G; Computer Engineering and Informatics Department, University of Patras, Patras, Greece., Maragoudakis M; Department of Informatics, Ionian University, Corfu, Greece., Mylonas P; Department of Informatics and Computer Engineering, University of West Attica, Athens, Greece.
Source:
Advances in experimental medicine and biology [Adv Exp Med Biol] 2026; Vol. 1487, pp. 529-536.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 0121103 Publication Model: Print Cited Medium: Print ISSN: 0065-2598 (Print) Linking ISSN: 00652598 NLM ISO Abbreviation: Adv Exp Med Biol Subsets: MEDLINE
Imprint Name(s):
Publication: 1998- : New York : Kluwer Academic/Plenum Publishers
Original Publication: New York, Plenum Press.
Original Publication: New York, Plenum Press.
MeSH Terms:
Brain Neoplasms*/classification , Brain Neoplasms*/diagnostic imaging , Brain Neoplasms*/pathology , Meningioma*/diagnostic imaging , Meningioma*/classification , Meningioma*/pathology , Glioma*/diagnostic imaging , Glioma*/classification , Glioma*/pathology , Image Interpretation, Computer-Assisted*/methods , Neural Networks, Computer* , Deep Learning*, Humans ; Machine Learning ; Magnetic Resonance Imaging ; Convolutional Neural Networks
References:
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Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS (2021) Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8:3161–3183. (PMID: 10.1007/s40747-021-00563-y)
Bangare SL, Pradeepini G, Patil ST (2017) Brain tumor classification using mixed method approach. In: International conference on information communication and embedded systems (ICICES). IEEE, Boca Raton, pp 1–4.
Bondy ML, Scheurer ME, Malmer B, Barnholtz-Sloan JS, Davis FG, Il’yasova D, Carol Kruchko BJM, Rajaraman P, Schwartzbaum JA, Sadetzki S, Schlehofer B, Tihan T, Wiemels JL, Wrensch M, Buffler PA (2008) Brain tumor epidemiology: consensus from the brain tumor epidemiology consortium. Cancer 113(S7):1953–1968. (PMID: 10.1002/cncr.2374118798534)
Deepak S, Ameer PM (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345. (PMID: 10.1016/j.compbiomed.2019.10334531279167)
Deniz E, Sengür A, Kadiroglu Z, Guo Y, Bajaj V, Budak Ü (2018) Transfer learning based histopathologic image classification for breast cancer detection. Health Inform Sci Syst 6(1):18. (PMID: 10.1007/s13755-018-0057-x)
Desai M, Shah M (2021) An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and convolutional neural network (CNN). Clin eHealth 4:1–11. (PMID: 10.1016/j.ceh.2020.11.002)
Febrianto DC, Soesanti I, Nugroho HA (2020) Convolutional neural network for brain tumor detection. In: IOP conference series: materials science and engineering, vol 771, p 012031.
Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 21(1–3):43–63. (PMID: 10.1016/S0933-3657(00)00073-711154873)
Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426–1438. (PMID: 10.1016/j.mri.2013.05.00223790354)
Gu X, Shen Z, Xue J, Fan Y, Ni T (2021) Brain tumor MR image classification using convolutional dictionary learning with local constraint. Front Neurosci 15:679847. (PMID: 10.3389/fnins.2021.679847341220018193950)
Hemanth DJ, Anitha J (2019) Modified genetic algorithm approaches for classification of abnormal magnetic resonance brain tumour images. Appl Soft Comput 75:21–28. (PMID: 10.1016/j.asoc.2018.10.054)
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167.
Jain R, Jain N, Aggarwal A, Hemanth DJ (2019) Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn Syst Res 57:147–159. (PMID: 10.1016/j.cogsys.2018.12.015)
Kanavos A, Mylonas P (2023) Deep learning analysis of histopathology images for breast cancer detection: a comparative study of ResNet and VGG architectures. In: 18th international workshop on semantic and social media adaptation and personalization (SMAP). IEEE, Boca Raton, pp 1–6.
Kanavos A, Kolovos E, Papadimitriou O, Maragoudakis M (2022) Breast cancer classification of histopathological images using deep convolutional neural networks. In: 7th South-East Europe design automation, computer engineering, computer networks and social media conference (SEEDA-CECNSM). IEEE, Boca Raton, pp 1–6.
Kanavos A, Papadimitriou O, Kaponis A, Maragoudakis M (2023a) Enhancing disease diagnosis: a CNN-based approach for automated white blood cell classification. In: IEEE international conference on big data, pp 4606–4613.
Kanavos A, Papadimitriou O, Maragoudakis M (2023b) Enhancing COVID-19 diagnosis from chest x-ray images using deep convolutional neural networks. In: 18th international workshop on semantic and social media adaptation and personalization (SMAP). IEEE, Boca Raton, pp 1–6.
Kanavos A, Papadimitriou O, Mylonas P, Maragoudakis M (2023c) Enhancing sign language recognition using deep convolutional neural networks. In: 14th international conference on information, intelligence, systems & applications (IISA). IEEE, Boca Raton, pp 1–4.
Kang J, Ullah Z, Gwak J (2021) MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6):2222. (PMID: 10.3390/s21062222338101768004778)
Kaur T, Gandhi TK (2020) Deep convolutional neural networks with transfer learning for automated brain image classification. Mach Vision Appl 31(3):20. (PMID: 10.1007/s00138-020-01069-2)
Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T, Zahoor S, Akram T (2019) Brain tumor detection and classification: a framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82(6):909–922. (PMID: 10.1002/jemt.2323830801840)
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica 114:97–109. (PMID: 10.1007/s00401-007-0243-4176184411929165)
Mabray MC, Barajas RF, Cha S (2015) Modern brain tumor imaging. Brain Tumor Res Treat 3(1):8–23. (PMID: 10.14791/btrt.2015.3.1.8259779024426283)
Manaswi NK (2018) Understanding and working with Keras. In: Deep learning with applications using python, pp 31–43.
Mathesul S, Swain D, Satapathy SK, Rambhad A, Acharya B, Gerogiannis VC, Kanavos A (2023) COVID-19 detection from chest x-ray images based on deep learning techniques. Algorithms 16(10):494. (PMID: 10.3390/a16100494)
Pang B, Nijkamp E, Wu YN (2020) Deep learning with TensorFlow: a review. J Educ Behav Stat 45(2):227–248. (PMID: 10.3102/1076998619872761)
Papadimitriou O, Kanavos A, Maragoudakis M (2023a) Automated pneumonia detection from chest X-ray images using deep convolutional neural networks. In: 14th international conference on information, intelligence, systems & applications (IISA). IEEE, Boca Raton, pp 1–4.
Papadimitriou O, Kanavos A, Mylonas P, Maragoudakis M (2023b) Advancing weather image classification using deep convolutional neural networks. In: 18th international workshop on semantic and social media adaptation and personalization (SMAP). IEEE, Boca Raton, pp 1–6.
Papadimitriou O, Kanavos A, Mylonas P, Maragoudakis M (2023c) Classification of Alzheimer’s disease subjects from MRI using deep convolutional neural networks. In: 3rd international conference on novel & intelligent digital systems (NiDS). Lecture notes in networks and systems, vol 784, pp 277–286. (PMID: 10.1007/978-3-031-44146-2_28)
Papadimitriou O, Kanavos A, Maragoudakis M, Gerogiannis VC (2024) Chess piece recognition using deep convolutional neural networks. In: 4th symposium on pattern recognition and applications (SPRA), vol 13162, p 1316202.
Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar K (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017. (PMID: 10.1109/ACCESS.2020.2981337)
Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182. (PMID: 10.1016/j.jocs.2018.12.003)
Savvopoulos A, Kanavos A, Mylonas P, Sioutas S (2018) LSTM accelerator for convolutional object identification. Algorithms 11(10):157. (PMID: 10.3390/a11100157)
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958.
Weizman L, Sira LB, Joskowicz L, Rubin DL, Yeom KW, Constantini S, Shofty B, Bashat DB (2014) Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 41(5):052303. (PMID: 10.1118/1.4871040247843964000396)
Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS (2021) Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8:3161–3183. (PMID: 10.1007/s40747-021-00563-y)
Bangare SL, Pradeepini G, Patil ST (2017) Brain tumor classification using mixed method approach. In: International conference on information communication and embedded systems (ICICES). IEEE, Boca Raton, pp 1–4.
Bondy ML, Scheurer ME, Malmer B, Barnholtz-Sloan JS, Davis FG, Il’yasova D, Carol Kruchko BJM, Rajaraman P, Schwartzbaum JA, Sadetzki S, Schlehofer B, Tihan T, Wiemels JL, Wrensch M, Buffler PA (2008) Brain tumor epidemiology: consensus from the brain tumor epidemiology consortium. Cancer 113(S7):1953–1968. (PMID: 10.1002/cncr.2374118798534)
Deepak S, Ameer PM (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345. (PMID: 10.1016/j.compbiomed.2019.10334531279167)
Deniz E, Sengür A, Kadiroglu Z, Guo Y, Bajaj V, Budak Ü (2018) Transfer learning based histopathologic image classification for breast cancer detection. Health Inform Sci Syst 6(1):18. (PMID: 10.1007/s13755-018-0057-x)
Desai M, Shah M (2021) An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and convolutional neural network (CNN). Clin eHealth 4:1–11. (PMID: 10.1016/j.ceh.2020.11.002)
Febrianto DC, Soesanti I, Nugroho HA (2020) Convolutional neural network for brain tumor detection. In: IOP conference series: materials science and engineering, vol 771, p 012031.
Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 21(1–3):43–63. (PMID: 10.1016/S0933-3657(00)00073-711154873)
Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426–1438. (PMID: 10.1016/j.mri.2013.05.00223790354)
Gu X, Shen Z, Xue J, Fan Y, Ni T (2021) Brain tumor MR image classification using convolutional dictionary learning with local constraint. Front Neurosci 15:679847. (PMID: 10.3389/fnins.2021.679847341220018193950)
Hemanth DJ, Anitha J (2019) Modified genetic algorithm approaches for classification of abnormal magnetic resonance brain tumour images. Appl Soft Comput 75:21–28. (PMID: 10.1016/j.asoc.2018.10.054)
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167.
Jain R, Jain N, Aggarwal A, Hemanth DJ (2019) Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn Syst Res 57:147–159. (PMID: 10.1016/j.cogsys.2018.12.015)
Kanavos A, Mylonas P (2023) Deep learning analysis of histopathology images for breast cancer detection: a comparative study of ResNet and VGG architectures. In: 18th international workshop on semantic and social media adaptation and personalization (SMAP). IEEE, Boca Raton, pp 1–6.
Kanavos A, Kolovos E, Papadimitriou O, Maragoudakis M (2022) Breast cancer classification of histopathological images using deep convolutional neural networks. In: 7th South-East Europe design automation, computer engineering, computer networks and social media conference (SEEDA-CECNSM). IEEE, Boca Raton, pp 1–6.
Kanavos A, Papadimitriou O, Kaponis A, Maragoudakis M (2023a) Enhancing disease diagnosis: a CNN-based approach for automated white blood cell classification. In: IEEE international conference on big data, pp 4606–4613.
Kanavos A, Papadimitriou O, Maragoudakis M (2023b) Enhancing COVID-19 diagnosis from chest x-ray images using deep convolutional neural networks. In: 18th international workshop on semantic and social media adaptation and personalization (SMAP). IEEE, Boca Raton, pp 1–6.
Kanavos A, Papadimitriou O, Mylonas P, Maragoudakis M (2023c) Enhancing sign language recognition using deep convolutional neural networks. In: 14th international conference on information, intelligence, systems & applications (IISA). IEEE, Boca Raton, pp 1–4.
Kang J, Ullah Z, Gwak J (2021) MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6):2222. (PMID: 10.3390/s21062222338101768004778)
Kaur T, Gandhi TK (2020) Deep convolutional neural networks with transfer learning for automated brain image classification. Mach Vision Appl 31(3):20. (PMID: 10.1007/s00138-020-01069-2)
Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T, Zahoor S, Akram T (2019) Brain tumor detection and classification: a framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82(6):909–922. (PMID: 10.1002/jemt.2323830801840)
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica 114:97–109. (PMID: 10.1007/s00401-007-0243-4176184411929165)
Mabray MC, Barajas RF, Cha S (2015) Modern brain tumor imaging. Brain Tumor Res Treat 3(1):8–23. (PMID: 10.14791/btrt.2015.3.1.8259779024426283)
Manaswi NK (2018) Understanding and working with Keras. In: Deep learning with applications using python, pp 31–43.
Mathesul S, Swain D, Satapathy SK, Rambhad A, Acharya B, Gerogiannis VC, Kanavos A (2023) COVID-19 detection from chest x-ray images based on deep learning techniques. Algorithms 16(10):494. (PMID: 10.3390/a16100494)
Pang B, Nijkamp E, Wu YN (2020) Deep learning with TensorFlow: a review. J Educ Behav Stat 45(2):227–248. (PMID: 10.3102/1076998619872761)
Papadimitriou O, Kanavos A, Maragoudakis M (2023a) Automated pneumonia detection from chest X-ray images using deep convolutional neural networks. In: 14th international conference on information, intelligence, systems & applications (IISA). IEEE, Boca Raton, pp 1–4.
Papadimitriou O, Kanavos A, Mylonas P, Maragoudakis M (2023b) Advancing weather image classification using deep convolutional neural networks. In: 18th international workshop on semantic and social media adaptation and personalization (SMAP). IEEE, Boca Raton, pp 1–6.
Papadimitriou O, Kanavos A, Mylonas P, Maragoudakis M (2023c) Classification of Alzheimer’s disease subjects from MRI using deep convolutional neural networks. In: 3rd international conference on novel & intelligent digital systems (NiDS). Lecture notes in networks and systems, vol 784, pp 277–286. (PMID: 10.1007/978-3-031-44146-2_28)
Papadimitriou O, Kanavos A, Maragoudakis M, Gerogiannis VC (2024) Chess piece recognition using deep convolutional neural networks. In: 4th symposium on pattern recognition and applications (SPRA), vol 13162, p 1316202.
Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar K (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017. (PMID: 10.1109/ACCESS.2020.2981337)
Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182. (PMID: 10.1016/j.jocs.2018.12.003)
Savvopoulos A, Kanavos A, Mylonas P, Sioutas S (2018) LSTM accelerator for convolutional object identification. Algorithms 11(10):157. (PMID: 10.3390/a11100157)
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958.
Weizman L, Sira LB, Joskowicz L, Rubin DL, Yeom KW, Constantini S, Shofty B, Bashat DB (2014) Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 41(5):052303. (PMID: 10.1118/1.4871040247843964000396)
Contributed Indexing:
Keywords: Automated tumor diagnosis; Brain tumor classification; Convolutional neural networks; Deep learning; Medical image analysis
Entry Date(s):
Date Created: 20251122 Date Completed: 20251122 Latest Revision: 20251122
Update Code:
20260130
DOI:
10.1007/978-3-032-03398-7_48
PMID:
41273590
Database:
MEDLINE
Weitere Informationen
Accurate brain tumor classification is crucial for advancing diagnostic precision and streamlining treatment strategies. This chapter presents a brain tumor image classification methodology leveraging deep learning techniques, specifically convolutional neural networks (CNNs). Our method exploits CNNs to autonomously extract salient features from medical imaging data, enabling the differentiation of tumor types, including gliomas, meningiomas, and metastatic tumors. The architecture of our CNN comprises several convolutional layers, pooling layers, and fully connected layers designed to capture and interpret complex patterns in brain tumor imagery effectively. We enhance the model's performance through comprehensive data augmentation and rigorous hyperparameter tuning, achieving significant improvements in classification accuracy. Extensive experimental evaluations demonstrate the efficacy of our approach, underscoring its potential to significantly enhance diagnostic processes by providing accurate, automated tumor classification. The advancements detailed herein contribute to the broader application of machine learning in medical imaging, promising substantial impacts on patient care and treatment optimization.
(© 2026. The Author(s), under exclusive license to Springer Nature Switzerland AG.)