Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
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Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy-preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open-source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201-based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor.
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Aggarwal, Meenakshi; Khullar, Vikas; Goyal, Nitin; Rastogi, Rashi; Singh, Aman; Yélamos Torres, Vanessa y Albahar, Marwan Ali
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, vanessa.yelamos@funiber.org, SIN ESPECIFICAR
(2023)
Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification.
International Journal of Imaging Systems and Technology, 34 (2).
ISSN 0899-9457
Resumen
Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy-preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open-source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201-based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor.
| Tipo de Documento: | Artículo |
|---|---|
| Clasificación temática: | Materias > Ingeniería |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica |
| Depositado: | 04 Dic 2024 23:30 |
| Ultima Modificación: | 04 Dic 2024 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/15505 |
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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.
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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.
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Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
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Luo
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What works in financial education? Experimental evidence on program impact
Financial education is increasingly essential for safeguarding both individual and corporate well-being. This study systematically reviews global financial education experiments using a dual-method framework that integrates a deep learning classifier with advanced multivariate statistical techniques. Our analysis indicates that while short-term improvements in financial literacy are common, such gains tend to diminish over time without ongoing reinforcement. Moreover, the limited impact of digital innovations and monetary incentives suggests that successful financial education depends on more than simply deploying technological solutions or extrinsic rewards. Overall, this review provides valuable insights into the evolving landscape of financial education in a dynamic economic context and underscores the need for sustainable strategies that secure lasting improvements in financial literacy.
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An improved hybrid image steganography method using AES algorithm
Image steganography is the process of hiding information, which can be text, image, or video inside a cover image. Recent steganography literature hasn’t addressed the problem of loss of secret information during extraction and reliability. Hence, to reduce information loss and provide reliability between in the basic criteria, Herein, we proposed a hybrid method that utilizes the least significant bit (LSB) substitution, transppsition, magic matrix, key and Advance Encrytion Standard (AES) algorithm. The LSB method decreases embedding errors by implementing a new value difference algorithm. In addition, to improves the reliability between the basic criterion for image steganography we used transposition, magic matrix, key and AES. The proposed method ensures a high-quality image format in the RGB color model to conceal the hidden message within the cover image which is jpeg. The proposed hybrid method performed several experiments and these are mainly based on quality assessment metrics such as PSNR, SSIM, RMSE, NCC, etc. which showed better results. The proposed method also analyzed with different perspectives in terms of different dimensions of images and different sizes of message text which showed better results. In addition, the performance of the proposed method showed better results based on (regular and singular) steganalysis, noise, and cropping attacks. The security analyses such as key space, differential, and statistical attacks show that the proposed scheme is secure and robust against channel noise and JPEG compression.
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