Improved brain tumor classification through DenseNet121 based transfer learning

Brain tumors have a big effect on a person’s health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very important for effective treatment. Many of the current diagnostic methods are time-consuming, rely primarily on hand interpretation, and freq...

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Published in:	Discover. Oncology Vol. 16; no. 1; pp. 1645 - 23
Main Authors:	Rasheed, Mehwish, Jaffar, Muhammad Arfan, Akram, Arslan, Rashid, Javed, Alshalali, Tagrid Abdullah N., Irshad, Asma, Sarwar, Nadeem
Format:	Journal Article
Language:	English
Published:	New York Springer US 27.08.2025 Springer Nature B.V Springer
Subjects:	Accuracy Algorithms Automation Brain cancer Brain research Cancer Research Classification Datasets Deep learning DenseNet121 Glioma Illnesses Internal Medicine Literature reviews Machine learning Magnetic resonance imaging Medical imaging Medicine Medicine & Public Health Molecular Medicine Multiclass brain tumor classification Oncology Radiotherapy Support vector machines Surgical Oncology Transfer learning Tumors Deep learning Transfer learning Multiclass brain tumor classification DenseNet121
ISSN:	2730-6011, 2730-6011
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Abstract	Brain tumors have a big effect on a person’s health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very important for effective treatment. Many of the current diagnostic methods are time-consuming, rely primarily on hand interpretation, and frequently yield unsatisfactory results. This work finds brain tumors in MRI data using DenseNet121 architecture with transfer learning. Model training made use of the Kaggle dataset. By preprocessing the stage, resizing the MRI pictures to minimize noise would help the model perform better. From one MRI scan, the proposed approach divides brain tissues into four groups: benign tumors, gliomas, meningiomas, and pituitary gland malignancies. The designed DenseNet121 architecture precisely classifies brain cancers. We assessed the models’ performance in terms of accuracy, precision, recall, and F1-score. The suggested approach proved successful in the multi-class categorization of brain tumors since it attained an average accuracy improvement of 96.90%. Unlike previous diagnostic techniques, such as eye inspection and other machine learning models, the proposed DenseNet121-based approach is more accurate, takes less time to analyze, and requires less human input. Although the automated method ensures consistent and predictable results, human error sometimes causes more unpredictability in conventional methods. Based on MRI-based detection and transfer learning, this paper proposes an automated method for the classification of brain cancers. The method improves the precision and speed of brain tumor diagnosis, which benefits both MRI-based classification research and clinical use. The development of deep-learning models may even further improve tumor identification and prognosis prediction.
AbstractList	Brain tumors have a big effect on a person's health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very important for effective treatment. Many of the current diagnostic methods are time-consuming, rely primarily on hand interpretation, and frequently yield unsatisfactory results. This work finds brain tumors in MRI data using DenseNet121 architecture with transfer learning. Model training made use of the Kaggle dataset. By preprocessing the stage, resizing the MRI pictures to minimize noise would help the model perform better. From one MRI scan, the proposed approach divides brain tissues into four groups: benign tumors, gliomas, meningiomas, and pituitary gland malignancies. The designed DenseNet121 architecture precisely classifies brain cancers. We assessed the models' performance in terms of accuracy, precision, recall, and F1-score. The suggested approach proved successful in the multi-class categorization of brain tumors since it attained an average accuracy improvement of 96.90%. Unlike previous diagnostic techniques, such as eye inspection and other machine learning models, the proposed DenseNet121-based approach is more accurate, takes less time to analyze, and requires less human input. Although the automated method ensures consistent and predictable results, human error sometimes causes more unpredictability in conventional methods. Based on MRI-based detection and transfer learning, this paper proposes an automated method for the classification of brain cancers. The method improves the precision and speed of brain tumor diagnosis, which benefits both MRI-based classification research and clinical use. The development of deep-learning models may even further improve tumor identification and prognosis prediction.Brain tumors have a big effect on a person's health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very important for effective treatment. Many of the current diagnostic methods are time-consuming, rely primarily on hand interpretation, and frequently yield unsatisfactory results. This work finds brain tumors in MRI data using DenseNet121 architecture with transfer learning. Model training made use of the Kaggle dataset. By preprocessing the stage, resizing the MRI pictures to minimize noise would help the model perform better. From one MRI scan, the proposed approach divides brain tissues into four groups: benign tumors, gliomas, meningiomas, and pituitary gland malignancies. The designed DenseNet121 architecture precisely classifies brain cancers. We assessed the models' performance in terms of accuracy, precision, recall, and F1-score. The suggested approach proved successful in the multi-class categorization of brain tumors since it attained an average accuracy improvement of 96.90%. Unlike previous diagnostic techniques, such as eye inspection and other machine learning models, the proposed DenseNet121-based approach is more accurate, takes less time to analyze, and requires less human input. Although the automated method ensures consistent and predictable results, human error sometimes causes more unpredictability in conventional methods. Based on MRI-based detection and transfer learning, this paper proposes an automated method for the classification of brain cancers. The method improves the precision and speed of brain tumor diagnosis, which benefits both MRI-based classification research and clinical use. The development of deep-learning models may even further improve tumor identification and prognosis prediction. Brain tumors have a big effect on a person's health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very important for effective treatment. Many of the current diagnostic methods are time-consuming, rely primarily on hand interpretation, and frequently yield unsatisfactory results. This work finds brain tumors in MRI data using DenseNet121 architecture with transfer learning. Model training made use of the Kaggle dataset. By preprocessing the stage, resizing the MRI pictures to minimize noise would help the model perform better. From one MRI scan, the proposed approach divides brain tissues into four groups: benign tumors, gliomas, meningiomas, and pituitary gland malignancies. The designed DenseNet121 architecture precisely classifies brain cancers. We assessed the models' performance in terms of accuracy, precision, recall, and F1-score. The suggested approach proved successful in the multi-class categorization of brain tumors since it attained an average accuracy improvement of 96.90%. Unlike previous diagnostic techniques, such as eye inspection and other machine learning models, the proposed DenseNet121-based approach is more accurate, takes less time to analyze, and requires less human input. Although the automated method ensures consistent and predictable results, human error sometimes causes more unpredictability in conventional methods. Based on MRI-based detection and transfer learning, this paper proposes an automated method for the classification of brain cancers. The method improves the precision and speed of brain tumor diagnosis, which benefits both MRI-based classification research and clinical use. The development of deep-learning models may even further improve tumor identification and prognosis prediction. Abstract Brain tumors have a big effect on a person’s health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very important for effective treatment. Many of the current diagnostic methods are time-consuming, rely primarily on hand interpretation, and frequently yield unsatisfactory results. This work finds brain tumors in MRI data using DenseNet121 architecture with transfer learning. Model training made use of the Kaggle dataset. By preprocessing the stage, resizing the MRI pictures to minimize noise would help the model perform better. From one MRI scan, the proposed approach divides brain tissues into four groups: benign tumors, gliomas, meningiomas, and pituitary gland malignancies. The designed DenseNet121 architecture precisely classifies brain cancers. We assessed the models’ performance in terms of accuracy, precision, recall, and F1-score. The suggested approach proved successful in the multi-class categorization of brain tumors since it attained an average accuracy improvement of 96.90%. Unlike previous diagnostic techniques, such as eye inspection and other machine learning models, the proposed DenseNet121-based approach is more accurate, takes less time to analyze, and requires less human input. Although the automated method ensures consistent and predictable results, human error sometimes causes more unpredictability in conventional methods. Based on MRI-based detection and transfer learning, this paper proposes an automated method for the classification of brain cancers. The method improves the precision and speed of brain tumor diagnosis, which benefits both MRI-based classification research and clinical use. The development of deep-learning models may even further improve tumor identification and prognosis prediction.
ArticleNumber	1645
Author	Alshalali, Tagrid Abdullah N. Sarwar, Nadeem Rashid, Javed Irshad, Asma Akram, Arslan Jaffar, Muhammad Arfan Rasheed, Mehwish
Author_xml	– sequence: 1 givenname: Mehwish surname: Rasheed fullname: Rasheed, Mehwish organization: Faculty of Computer Science and Information Technology, The Superior University – sequence: 2 givenname: Muhammad Arfan surname: Jaffar fullname: Jaffar, Muhammad Arfan organization: Faculty of Computer Science and Information Technology, The Superior University – sequence: 3 givenname: Arslan surname: Akram fullname: Akram, Arslan organization: Faculty of Computer Science and Information Technology, The Superior University, Department of Computer Science, University of People, MLC Lab – sequence: 4 givenname: Javed surname: Rashid fullname: Rashid, Javed organization: MLC Lab, Information Technology Services, University of Okara – sequence: 5 givenname: Tagrid Abdullah N. surname: Alshalali fullname: Alshalali, Tagrid Abdullah N. organization: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University – sequence: 6 givenname: Asma orcidid: 0000-0002-0594-5877 surname: Irshad fullname: Irshad, Asma organization: School of Biochemistry and Biotechnology, University of the Punjab – sequence: 7 givenname: Nadeem orcidid: 0000-0001-8681-6382 surname: Sarwar fullname: Sarwar, Nadeem email: nadeem_srwr@yahoo.com organization: Department of Computer Science, Bahria University Lahore Campus
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Keywords	Deep learning Transfer learning Multiclass brain tumor classification DenseNet121
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Snippet	Brain tumors have a big effect on a person’s health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very... Brain tumors have a big effect on a person's health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis is very... Abstract Brain tumors have a big effect on a person’s health by letting abnormal cells grow unchecked in the brain. This means that early and correct diagnosis...
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SubjectTerms	Accuracy Algorithms Automation Brain cancer Brain research Cancer Research Classification Datasets Deep learning DenseNet121 Glioma Illnesses Internal Medicine Literature reviews Machine learning Magnetic resonance imaging Medical imaging Medicine Medicine & Public Health Molecular Medicine Multiclass brain tumor classification Oncology Radiotherapy Support vector machines Surgical Oncology Transfer learning Tumors
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Title	Improved brain tumor classification through DenseNet121 based transfer learning
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