Glioma Detection using Improved Artificial Neural Network in MRI Images

Nandihal, Priya; S, Vijaya Shetty; Guha, Tapas; Pareek, Piyush Kumar

doi:10.1109/mysurucon55714.2022.9972712

Cited by 34 publications

(3 citation statements)

References 13 publications

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“…There are a sum of available tools for early illness diagnosis 1 . Intelligent requests, i.e., the evidence of medical knowledge, are not readily available to collect the necessary sets of data for illness analysis 2 . Recently, however, a method known as Machine Learning (ML) optimisation has emerged, making significant contributions to the prediction and resolution of nonlinear and complicated problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Jopate,

Pareek,

et al. 2024

Baghdad Sci.J

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كثيرًا ما يعاني الأشخاص الذين يعانون من قصور الغدة الدرقية من أعراض حادة. يؤدي التصنيف الصحيح والتعلم الآلي إلى تحسين تشخيص أمراض الغدة الدرقية بشكل كبير. سيؤثر هذا التصنيف الدقيق على تقديم الرعاية للمرضى في الوقت المناسب. على الرغم من وجود تقنيات التشخيص، فإنها تسعى في كثير من الأحيان إلى التصنيف الثنائي، وتستخدم مجموعات بيانات كبيرة غير كافية، وتفتقر إلى تأكيد استنتاجاتها. تركز الأساليب الحالية على تحسين النموذج، في حين يتم إهمال هندسة الميزات. يقدم هذا البحث نموذج خوارزمية تحسين قطيع الفيل التكيفي AEHOA لاختيار السمات المثالية من أجل التحايل على هذه القيود. في البداية، استخدم طريقة تسمى تقنية الإفراط في أخذ العينات للأقلية الاصطناعية SMOTE لتسوية البيانات. وأخيرًا، يتم إدخال معلمات نموذج AEHOA في الشبكة العصبية التلافيفية CNN لتصنيف البيانات وتعزيز التنبؤ. تمت أيضًا زيادة دقة تنبؤات التصنيف عن طريق تعديل مجموعة البيانات. تم إخضاع مجموعتي البيانات لعملية تصنيف لإجراء مقارنة أكثر دقة للنتائج.

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Section: Introductionmentioning

confidence: 99%

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Jopate,

Pareek,

et al. 2024

Baghdad Sci.J

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“…Data mining has helped the medical field thanks to the extremely high accuracy of the produced models [15]. Data mining has been shown to significantly improve the consequences achieved with existing approaches and contribute to new discoveries in several fields of medicine [16].…”

Section: Introductionmentioning

confidence: 99%

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Inamanamelluri,

Pulipati,

Pradhan

et al. 2023

RIA

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A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive.

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“…Gliomas can be low-grade or highgrade. High-grade gliomas tend to grow faster and are more difficult to treat [1,2].…”

Section: Introductionmentioning

confidence: 99%

Automated Grading of Glioma Using Deep Neural Networks

YILDIRIM,

ASLAN,

CENGİL

et al. 2023

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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Gliomas are one of the most common tumors in the brain. It is possible to grade gliomas as Lower-Grade Glioma (LGG) and Glioblastoma Multiforme (GBM). Clinical and molecular/mutation factors come to the fore in the grading of gliomas. Molecular tests used to grade glioma are expensive and time consuming. In this study, deep learning networks were used for glioma grading. Long short-term memory (LSTM) and Convolutional neural network (CNN) were used together in the proposed model. The developed model was also compared with 6 different classifiers accepted in the literature. Among the models used in the study, the developed model achieved the highest performance. In this study, glioma grading was performed for the purpose of improving performance and reducing costs.

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Glioma Detection using Improved Artificial Neural Network in MRI Images

Cited by 34 publications

References 13 publications

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Automated Grading of Glioma Using Deep Neural Networks

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