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Deep learningensemble 2D CNNapproach towards thedetection of lungcancer Asghar Ali ShahHafiz Abid Mahmood MalikAbdulHafeez Muhammad2023 год

Deep learning ensemble 2D CNN approach towards the detection of lung cancer
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Аннотация: In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.
Год издания: 2023
Авторы: Asghar Ali Shah, Hafiz Abid Mahmood Malik, AbdulHafeez Muhammad, Abdullah Alourani, Zaeem Arif Butt
Издательство: Nature Portfolio
Источник: Scientific Reports
Ключевые слова: COVID-19 diagnosis using AI, Lung Cancer Diagnosis and Treatment, Radiomics and Machine Learning in Medical Imaging
Другие ссылки: Scientific Reports (PDF)
Scientific Reports (HTML)
PubMed Central (HTML)
PubMed (HTML)