{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:06:50Z","timestamp":1789121210910,"version":"build-2803163510"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Biomedical Sensors and Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"id":[{"id":"https:\/\/ror.org\/01cq23130","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>A brain tumor is a life-threatening disease that carries a high mortality burden. It can be treated if detected in the early stages. Manual analysis by a radiologist using Magnetic Resonance Imaging (MRI) is effective but slow and subject to inter-observer variability. In order to automate it, various deep learning models, especially Convolutional Neural Networks (CNNs), have been widely used. However, most of them rely on frozen, ImageNet-based pretrained backbones, where only a small classification head is updated during training. This leaves a large segment of parameters unable to adapt to the domain shift between natural images and MRI scans. This limits how far such models can be modified or improved further. This makes the model highly parameter-centric and unsuitable for development. In addition, most studies fail to provide insights on architectural choices, which inhibits reproduction and modifications. In order to alleviate these issues, this study presents SERA-Net, a convolutional architecture for brain tumor classification using MRI scans. It is trained from scratch and quantifies the performance of each model using an incremental ablation study. It consists of four blocks comprising different components, such as Squeeze-and-Excite channel attention, residual connections, a wider classification head, etc. Rather than proposing new operations, the contribution lies in the systematic, ablation-driven combination and empirical validation of established architectural components for this specific task. The model was trained and evaluated on a publicly available MRI dataset. With just \u22485 million parameters, it achieved an accuracy of 95.44%, an F1-score of 0.953, and various near-ideal parameters. It was observed to outperform six pretrained CNN baselines by more than 5\u201310%, while using substantially fewer parameters. Various other analyses indicate the efficacy of the proposed approach in detecting brain tumors. The proposed approach provides a scalable design that can be adapted to different domains with limited modifications.<\/jats:p>","DOI":"10.3390\/bioengineering13091055","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T08:17:42Z","timestamp":1789114662000},"page":"1055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SERA-Net: Rethinking CNN Design for Brain Tumor Classification via Squeeze-and-Excite Attention and Residual Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1697-3773","authenticated-orcid":false,"given":"Jyoti Ranjan","family":"Sahoo","sequence":"first","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7809-2131","authenticated-orcid":false,"given":"Surendra Kumar","family":"Nanda","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-5685","authenticated-orcid":false,"given":"Ganapati","family":"Panda","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1196-385X","authenticated-orcid":false,"given":"Ashutosh","family":"Soni","sequence":"additional","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7597-131X","authenticated-orcid":false,"given":"Jayanti","family":"Rout","sequence":"additional","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-4333","authenticated-orcid":false,"given":"Manob Jyoti","family":"Saikia","sequence":"additional","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Electrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"Cancer statistics, 2025","volume":"75","author":"Siegel","year":"2025","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","unstructured":"Ervik, M., Laversanne, M., Lam, F., Colombet, M., Ferlay, J., Filho, A.M., and Bray, F. (2026, May 28). Global Cancer Observatory: Cancer Today (2024 Estimates, Version 1.0). Available online: https:\/\/gco.iarc.who.int\/today."},{"key":"ref_3","first-page":"1","article-title":"Medical Image Captioning using CvT and DistillGPT2","volume":"Volume 1","author":"Kar","year":"2024","journal-title":"Proceedings of the 2024 Second International Conference on Advances in Information Technology (ICAIT)"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100252","DOI":"10.1016\/j.neuri.2025.100252","article-title":"Advances in deep learning for multimodal brain imaging: A comprehensive survey","volume":"6","author":"Balsabti","year":"2026","journal-title":"Neurosci. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109072","DOI":"10.1016\/j.bspc.2025.109072","article-title":"Enhancing brain tumor diagnosis: The role of topological features in deep learning approaches","volume":"114","author":"Joshi","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cmpb.2019.05.015","article-title":"Brain tumor detection using statistical and machine learning method","volume":"177","author":"Amin","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1002\/jemt.23688","article-title":"Brain tumor detection and multi-classification using advanced deep learning techniques","volume":"84","author":"Sadad","year":"2021","journal-title":"Microsc. Res. Tech."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101940","DOI":"10.1016\/j.compmedimag.2021.101940","article-title":"Role of deep learning in brain tumor detection and classification (2015 to 2020): A review","volume":"91","author":"Nazir","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104356","DOI":"10.1016\/j.bspc.2022.104356","article-title":"SSO-RBNN driven brain tumor classification with Saliency-K-means segmentation technique","volume":"81","author":"Nanda","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Balabantaray, S., Rath, M., Samal, T., and Mishra, M.R. (2023). Brain Tumour Ordering Using Visual Geometry Group Network. Proceedings of the 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), IEEE.","DOI":"10.1109\/ICSSAS57918.2023.10331783"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39873","DOI":"10.1109\/ACCESS.2026.3671448","article-title":"Optimizing Neural Networks for Brain Tumor Detection in MR Images Using Evolutionary Computing","volume":"14","author":"Durinec","year":"2026","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Panda, N.R., Muduli, D., Parija, S., Sharma, S.K., and Ranjan Kumar, R. (2025). DeepWave: A Hybrid CNN-Wavelet Approach for Brain Tumor MRI Classification. Proceedings of the 2025 International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), IEEE.","DOI":"10.1109\/IC-CGU67042.2025.11338053"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108466","DOI":"10.1016\/j.bspc.2025.108466","article-title":"A novel pruning-enhanced hybrid approach for efficient and accurate brain tumor diagnosis","volume":"112","author":"Dogan","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s44163-025-00753-4","article-title":"Automated brain tumor detection using advanced deep learning models","volume":"6","author":"Uniyal","year":"2026","journal-title":"Discov. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"063046","DOI":"10.1117\/1.JEI.33.6.063046","article-title":"Internet of Medical Things-enabled brain tumor classification using GWO-based optimized RBF network","volume":"33","author":"Panda","year":"2024","journal-title":"J. Electron. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115322","DOI":"10.1016\/j.knosys.2026.115322","article-title":"A novel hybrid machine and deep learning approach for brain tumor classification based patient survival time prediction","volume":"337","author":"Chitra","year":"2026","journal-title":"Knowl.-Based Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.aej.2026.01.003","article-title":"Brain tumor segmentation and classification: A CVAE-UNETR-ResNet50-VGG16 hybrid deep learning approach","volume":"135","author":"Salama","year":"2026","journal-title":"Alex. Eng. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6988","DOI":"10.1109\/TCE.2026.3691633","article-title":"ViT-CNN: Explainable Dual-Stream Cross-Attention for MRI Brain Tumor Screening on Consumer Edge Devices","volume":"72","author":"Zada","year":"2026","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108636","DOI":"10.1016\/j.bspc.2025.108636","article-title":"Enhancing brain tumor classification with a novel attention based explainable deep learning framework","volume":"112","author":"Hasan","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","first-page":"60","article-title":"Hybrid Quantum Graph Neural Network for Brain Tumor MR Image Classification","volume":"84","author":"Rajamohana","year":"2025","journal-title":"J. Sci. Ind. Res. (JSIR)"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abdusalomov, A.B., Mukhiddinov, M., and Whangbo, T.K. (2023). Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging. Cancers, 15.","DOI":"10.3390\/cancers15164172"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Taha, A.M., and Aly, S.A. (2026). Detecting Glioma, Meningioma, Pituitary Tumors, and Normal Brain Tissues based on YOLOV8 and YOLOV11 Deep Learning Models. Proceedings of the 2026 International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET), IEEE.","DOI":"10.1109\/ICAISET66439.2026.11542025"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20151","DOI":"10.1038\/s41598-026-48825-4","article-title":"YOLO-LS: A novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging","volume":"16","author":"Chen","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_24","unstructured":"Nickparvar, M. (2026, May 27). Brain Tumor MRI Dataset. Kaggle Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3931","DOI":"10.1007\/s11831-025-10416-3","article-title":"Deep learning and machine learning for brain tumor detection: A review, challenges, and future directions","volume":"33","author":"Mohsen","year":"2026","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yasmin, F., Xue, Y., Hasan, M., Sinha, A., Ding, W., and Liu, H. (2026). EvoBTSeg: Evolutionary Architecture Search for Compact and Precise Heterogeneous Brain Tumor Segmentation. IEEE Trans. Emerg. Top. Comput. Intell., early access.","DOI":"10.1109\/TETCI.2026.3692594"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:06:09Z","timestamp":1789117569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bioengineering13091055"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13091055","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}