Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

Joshi, Amol Avinash; Aziz, Rabia Musheer

doi:10.1002/ima.23007

Cited by 19 publications

(3 citation statements)

References 52 publications

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“…Accordingly, mono-WD POM merits further in vivo research on imaging efficacy and possible toxicity, which may facilitate the development of inexpensive, safe, and innovative CT contrast agents for clinical application. Finally, future research directions could include the latest potential strategies like deep learning to improve novel contrast agents’ effectiveness and potential innovations in imaging and biomaterial technologies [ 49 , 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Monolacunary Wells-Dawson Polyoxometalate as a Novel Contrast Agent for Computed Tomography: A Comprehensive Study on In Vivo Toxicity and Biodistribution

Stojanović,

Čolović,

Lalatović

et al. 2024

IJMS

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Polyoxotungstate nanoclusters have recently emerged as promising contrast agents for computed tomography (CT). In order to evaluate their clinical potential, in this study, we evaluated the in vitro CT imaging properties, potential toxic effects in vivo, and tissue distribution of monolacunary Wells–Dawson polyoxometalate, α2-K10P2W17O61.20H2O (mono-WD POM). Mono-WD POM showed superior X-ray attenuation compared to other tungsten-containing nanoclusters (its parent WD-POM and Keggin POM) and the standard iodine-based contrast agent (iohexol). The calculated X-ray attenuation linear slope for mono-WD POM was significantly higher compared to parent WD-POM, Keggin POM, and iohexol (5.97 ± 0.14 vs. 4.84 ± 0.05, 4.55 ± 0.16, and 4.30 ± 0.09, respectively). Acute oral (maximum-administered dose (MAD) = 960 mg/kg) and intravenous administration (1/10, 1/5, and 1/3 MAD) of mono-WD POM did not induce unexpected changes in rats’ general habits or mortality. Results of blood gas analysis, CO-oximetry status, and the levels of electrolytes, glucose, lactate, creatinine, and BUN demonstrated a dose-dependent tendency 14 days after intravenous administration of mono-WD POM. The most significant differences compared to the control were observed for 1/3 MAD, being approximately seventy times higher than the typically used dose (0.015 mmol W/kg) of tungsten-based contrast agents. The highest tungsten deposition was found in the kidney (1/3 MAD—0.67 ± 0.12; 1/5 MAD—0.59 ± 0.07; 1/10 MAD—0.54 ± 0.05), which corresponded to detected morphological irregularities, electrolyte imbalance, and increased BUN levels.

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Section: Discussionmentioning

confidence: 99%

Monolacunary Wells-Dawson Polyoxometalate as a Novel Contrast Agent for Computed Tomography: A Comprehensive Study on In Vivo Toxicity and Biodistribution

Stojanović,

Čolović,

Lalatović

et al. 2024

IJMS

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“…Notably, they managed to gain 98.2% accuracy, 97.9% specificity, and 96.59% sensitivity, which was better than the compared model's performance. Joshi et al [17] applied a deep learning approach to classify brain tumors with the help of gene expression data. An accuracy of 98.7% was obtained through the introduction of PSCS with deep learning.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Cancer Classification from Gene Expression Using Ensemble Learning with an Influential Feature Selection Technique

Tabassum,

Kamal,

Akhand

et al. 2024

BioMedInformatics

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Uncontrolled abnormal cell growth, known as cancer, may lead to tumors, immune system deterioration, and other fatal disability. Early cancer identification makes cancer treatment easier and increases the recovery rate, resulting in less mortality. Gene expression data play a crucial role in cancer classification at an early stage. Accurate cancer classification is a complex and challenging task due to the high-dimensional nature of the gene expression data relative to the small sample size. This research proposes using a dimensionality-reduction technique to address this limitation. Specifically, the mutual information (MI) technique is first utilized to select influential biomarker genes. Next, an ensemble learning model is applied to the reduced dataset using only the most influential features (genes) to develop an effective cancer classification model. The bagging method, where the base classifiers are Multilayer Perceptrons (MLPs), is chosen as an ensemble technique. The proposed cancer classification model, the MI-Bagging method, is applied to several benchmark gene expression datasets containing distinctive cancer classes. The cancer classification accuracy of the proposed model is compared with the relevant existing methods. The experimental results indicate that the proposed model outperforms the existing methods, and it is effective and competent for cancer classification despite the limited size of gene expression data with high dimensionality. The highest accuracy achieved by the proposed method demonstrates that the proposed emerging gene-expression-based cancer classifier has the potential to help in cancer treatment and lead to a higher cancer survival rate in the future.

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“…A Grouping Genetic Algorithm is proposed to solve a maximally diverse grouping problem for gene selection [36]. Compared to the individual original algorithms, the performance of improved and hybrid optimization algorithms such as Dynamic Harris Hawks Optimization with Mutation Mechanism [37], hybridization of cuckoo search with particle swarm optimization [38], sine cosine algorithm [39], and spider monkey optimization [40] are better in solving classification problems.…”

Section: Related Workmentioning

confidence: 99%

Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

Bharanidharan,

Chakravarthy,

Venkatesan

et al. 2024

IEEE Access

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Gene expression analysis can be used as a tool to detect cancer and the type of cancer in its early stages. However, the computational complexity for gene expression analysis is quite high due to the large amount of numerical data. Hence, dimensionality reduction techniques are employed to reduce the computational complexity, which in turns reduces the accuracy of cancer diagnosis. This research work focuses on the development of computerized tools with reduced computational complexity and increased accuracy to identify cancer cells in five different regions, namely breast, liver, lung, prostate, and renal. Sparse autoencoder is implemented to reduce feature dimensions, and Remora Optimization is used to increase the performance of cancer diagnosis system through transformation of features. The local entropybased fitness function is utilized to evaluate the appropriateness of population in Remora Optimization algorithm, and it is considered as the important reason for performance improvement in the proposed cancer diagnosis system. 1027 patients are taken into consideration in this analysis of the microarray datasets, which were gathered from the Curated Microarray Database. Six distinct metrics are used to examine the classification performance, and four different supervised classifiers are investigated. When the suggested sparse autoencoder and local entropy-based Remora Optimization are applied, the average balanced accuracy of the examined classifiers is 93.4%. The average dimensions are decreased from 36045 to 80 dimensions using the suggested method. In the absence of the suggested methodology, the vanilla classifiers yield an average balanced accuracy of 82.7%.

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Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

Cited by 19 publications

References 52 publications

Monolacunary Wells-Dawson Polyoxometalate as a Novel Contrast Agent for Computed Tomography: A Comprehensive Study on In Vivo Toxicity and Biodistribution

Monolacunary Wells-Dawson Polyoxometalate as a Novel Contrast Agent for Computed Tomography: A Comprehensive Study on In Vivo Toxicity and Biodistribution

Cancer Classification from Gene Expression Using Ensemble Learning with an Influential Feature Selection Technique

Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

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