Detection and classification of brain tumor using a hybrid learning model in CT scan images
Artículo
Materias > Biomedicina
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Abierto
Inglés
Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model’s complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3–4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings.
metadata
Ghasemi, Roja; Islam, Naveed; Bayat, Samin; Shabir, Muhammad; Rahman, Shahid; Amin, Farhan; de la Torre, Isabel; Kuc Castilla, Ángel Gabriel y Ramírez-Vargas, Debora L.
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, angel.kuc@uneatlantico.es, debora.ramirez@unini.edu.mx
(2025)
Detection and classification of brain tumor using a hybrid learning model in CT scan images.
Scientific Reports, 15 (1).
ISSN 2045-2322
![[img]](http://repositorio.uneatlantico.es/style/images/fileicons/text.png) |
Texto
s41598-025-18979-8.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Descargar (2MB) |
Resumen
Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model’s complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3–4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | Healthcare; CNN models; AI-based cognitive neuroscience; Medical image processing of neuroimaging |
| Clasificación temática: | Materias > Biomedicina Materias > Ingeniería |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica |
| Depositado: | 21 Oct 2025 13:31 |
| Ultima Modificación: | 21 Oct 2025 13:31 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/17858 |
Acciones (logins necesarios)
 |
Ver Objeto |
<a class="ep_document_link" href="/17889/1/PIIS1879406825006344.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Introduction Cancer in older adults is often associated with functional limitations, geriatric syndromes, poor self-rated health, vulnerability, and frailty, and these conditions might worsen treatment-related side effects. Recent guidelines for patients with cancer during and after treatment have documented the beneficial effects of exercise to counteract certain side effects; however, little is known about the role of exercise during cancer treatment in older adults. Materials and Methods This is a multicentre randomised controlled trial in which 200 participants will be allocated to a control group or an intervention group (the sample size has been calculated to detect a clinical difference of 1 point in Short Physical Performance Battery (SPPB) score, assuming an α error of 0.05, a β error of 0.20, and a 10 % loss rate). Patients aged ≥70 years, diagnosed with any type of solid cancer and candidates for systemic treatment are eligible. Subjects in the intervention group are invited to participate in a 12-week supervised multicomponent exercise programme in addition to receiving usual care. Study assessments are conducted at baseline and three months. The primary outcome measure is physical function as assessed by the SPPB. Secondary outcome measures include comprehensive geriatric assessment scores (including social situation, basic and instrumental activities of daily living, cognitive function, depression, nutritional status, polypharmacy, geriatric syndromes, pain, and emotional distress), anthropometric characteristics, frailty status, physical fitness, physical activity, cognitive function, quality of life, fatigue, and nutritional status. Study assessments also include analysis of inflammatory, endocrine, and nutritional mediators in serum and plasma as potential frailty biomarkers at mRNA and protein levels and multiparametric flow cytometric analysis to measure immunosenescence markers on T and NK cells. Discussion This study seeks to extend our knowledge on exercise interventions during systemic anticancer treatment in patients over 70 years of age. Results from this research will guide the management of older adults during systemic treatment in hospitals seeking to enhance the standard of care.
Julia García-García mail , Ana Rodriguez-Larrad mail , Maren Martinez de Rituerto Zeberio mail , Jenifer Gómez Mediavilla mail , Borja López-San Vicente mail , Nuria Torrego Artola mail , Izaskun Zeberio Etxetxipia mail , Irati Garmendia mail , Ainhoa Alberro mail , David Otaegui mail , Francisco Borrego Rabasco mail , María M. Caffarel mail , Kalliopi Vrotsou mail , Jon Irazusta mail , Haritz Arrieta mail , Mireia Peláez mail mireia.pelaez@uneatlantico.es, Jon Belloso mail , Laura Basterretxea mail ,
García-García
<a class="ep_document_link" href="/27825/1/s41598-026-39196-x_reference.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.
Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,
Butt
<a href="/27826/1/s41598-025-34766-x_reference.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Securing internet of things devices using a hybrid approach
With increased Internet of Things (IoT) devices, complexity and protection are more challenging. Lightweight cryptographic algorithms are secure and suitable for limited-resource environments; however, their hash functions provide encrypted data but not integrity. Strong security features are available, but setup is difficult and expensive. Network security mechanisms increase power consumption and latency. As IoT networks grow, managing cryptographic keys and securely authenticating large numbers of devices become complex tasks. Efficient key management strategies are required to ensure the scalability required. Existing state-of-the-art solutions lack standardization, scalability, complex and costly. Thus, this research proposes a secure solution for IoT resource-constrained devices, combining strong data integrity and lightweight encryption, and is thus named a hybrid. This hybrid approach integrates SHA-512 and the present cipher in our proposed approach and thus ensuring higher security than state-of-the-art models. This intelligent combination not only enhances the algorithm’s resistance against cryptographic attacks but also improves its processing speed. The proposed approach is used to reduce the processing time for encryption in the IoT platform and to preserve the trade-off between security and efficiency. In terms of memory use, execution time, and precision, the proposed approach is compared with recent state-of-the-art research. The experimental results indicate that our approach is efficient using the avalanche, authentication success rate, collision events, and execution time. The efficiency is 53% to 65%, and the avalanche effect indicates sensitivity to input variations, suggesting moderate-to-considerable reactivity to small data changes. The experimental tests conducted across 10,000 and 80,000 runs reveal no collisions and found that the proposed approach is resilient in managing unique IDs. Moreover, our approach performs consistently, with an average execution time of 0.088246 s, ranging from 0.075954 to 0.094583 s. Finally, our approach provides a practical and scalable solution for securing IoT devices in resource-constrained environments, addressing practical problems for IoT devices.
R. Sherine Jenny mail , N. Sugirtham mail , B. Thiyaneswaran mail , S. Kumarganesh mail , Martin Sagayam K. mail , Syed Immamul Ansarullah mail , Farhan Amin mail , Isabel de la Torre Díez mail , Carlos Manuel Osorio García mail carlos.osorio@uneatlantico.es, Alina Eugenia Pascual Barrera mail alina.pascual@unini.edu.mx,
Jenny
<a class="ep_document_link" href="/27827/1/Advanced%20Science%20-%202026%20-%20Wu%20-%20Constraint%20of%20Lignin%20Carbohydrate%20Complex%20Orchestrated%20on%20Polyphenol%20in%20Oil%20Water%20Interface.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
The therapeutic potential of polyphenols in ulcerative colitis (UC), mediated through immune modulation and gut microbiota homeostasis. To enhance the oral bioavailability of polyphenols, we architected a colon–targeted W1/O/W2 emulsion system featuring a rationally designed lignin–carbohydrate complex (LCC) as a dual–functional emulsifier system for the first time. Based on the innate structural duality of LCC, which comprising hydrophobic lignin and hydrophilic carbohydrates, we employed LCC for O/W emulsifier. This inherent amphiphilicity was further engineered via laccase–mediated grafting of isovanillin, yielding a modified LCC with tailored lipophilicity for effective W/O interfacial stabilization. The W1/O/W2 emulsion ensured the stability of the encapsulated polyphenols with divergent polarity but also enabled pH–responsive payload release under colonic conditions (pH >7.0). In DSS–induced colitis, the system demonstrated a synergistic effect, the LCC itself acted as a prebiotic to modulate the gut microbiota, specifically enriching short chain fatty acid–producing bacteria, while the released polyphenols reinforced the intestinal barrier, which collectively accelerated mucosal healing. This research proposes a carbon–neutral therapeutic strategy for colitis, not only establishing a proof–of–concept for replacing synthetic emulsifiers with engineered biomass, but also as a multi–functional platform to stabilize colon–targeted co–delivery system and microbiome regulation in colitis.
Qian Wu mail , Xingyu Zhang mail , Jingjia Zhang mail , Gaohui Huang mail , Chen Zhou mail , Chunlin Li mail , Xiaojun Huang mail , Jianbo Xiao mail , Nianjie Feng mail , Yuanbin She mail ,
Wu
en
close
Background Post-kala-azar dermal leishmaniasis (PKDL) is a skin condition that can become a complication in about 15 % of patients who have had kala-azar. Despite its significance, treatment options for PKDL are still limited. This systematic review and meta-analysis aim to evaluate the efficacy of amphotericin B for this condition. Methods PubMed, Embase, Cochrane, and Web of Science databases were searched for randomized controlled trials (RCTs) that reported the efficacy of Liposomal Amphotericin B in the treatment of PKDL. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Events per 100 observations with 95 % confidence intervals were performed for outcomes. Results Nine studies with 639 patients were included, the treatment durations ranging from 7 to 60 days. The mean age ranged from 9.2 to 31.0 years, and 359 patients were male. The PKDL treatment with liposomal amphotericin B resulted in a cure rate of 91.36 % (95 % CI: 76.60-97.15). However, a relapse was observed in 11.42 % (95 % CI: 6.20-20.8) of patients. Adverse events were common, with hepatic enzyme elevation (ALT/AST) being the most frequent (61.75 %; 95 % CI: 21.81–90.33), followed by fever in 29.93 % of cases (95 % CI: 5.09–77.30). Among the more serious side effects, decreased serum potassium was observed in 19.27 % (95 % CI: 3.84–58.82), and increased serum creatinine, indicative of nephrotoxicity, occurred in 15.08 % (95 % CI: 3.97–43.27). Nausea or vomiting, although less severe, affected 12.36 % of patients (95 % CI: 4.81–28.25). Conclusions These findings highlight that while liposomal amphotericin B is a potent therapeutic option for PKDL, its administration requires careful management and clinical vigilance to optimize outcomes and minimize risks.
Deivyd Vieira Silva Cavalcante mail , Lilia Maria Lima de Oliveira mail , Noor Husain mail , Beatriz Ximenes Mendes mail , Ana Clara Felix de Farias Santos mail , Luciana Borrigueiro mail , Lyria de Oliveira Rosa mail , Christian Ndikuryayo mail , Sarah Soares Amorim mail , Lalit Mohan mail , Fabiana Castro Porto Silva Lopes mail ,
Cavalcante