“…They also used a 10-fold cross-validation approach for the model’s training and had an accuracy of 96.56%. In a recent study [ 23 ], two different CNN models of 13 and 25 layers were proposed to classify brain images into two and five classes, respectively. With the increase in classes, the accuracy of the proposed model dropped to 92.66%.…”
With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.
“…They also used a 10-fold cross-validation approach for the model’s training and had an accuracy of 96.56%. In a recent study [ 23 ], two different CNN models of 13 and 25 layers were proposed to classify brain images into two and five classes, respectively. With the increase in classes, the accuracy of the proposed model dropped to 92.66%.…”
With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.
“…However, the need for a large dataset to train the models and the difficulty in interpreting the models hinders their usage in medical fields [163]. In terms of segmentation performance, it is evident from Tables 4 and 5 Aside from segmentation of brain tumor region from head MRI scan, classification of tumor into their respective histological type has great importance in diagnosis and treatment planning which actually requires biopsy procedure in today's medical practice [158]. Several methods which encompass shallow machine learning and deep learning have been proposed for brain tumor classification.…”
Section: Discussionmentioning
confidence: 99%
“…Aside from segmentation of brain tumor region from head MRI scan, classification of tumor into their respective histological type has great importance in diagnosis and treatment planning which actually requires biopsy procedure in today’s medical practice [ 158 ]. Several methods which encompass shallow machine learning and deep learning have been proposed for brain tumor classification.…”
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.
“…Current literature suggests that CNN models of DL are very efficient and successful for image classification problems [ 25 , 32 – 34 ]. A typical CNN model consists of two parts: feature extraction and classification.…”
Section: Methodsmentioning
confidence: 99%
“…Adaptive moment estimation algorithm (Adam) is used as optimizer in the proposed CNN model [ 36 ]. CNN hyper-parameters are tuned such that Mini-batch Size is 32 [ 37 , 38 ] Learning Rate is 0.001 [ 36 , 39 ], Momentum is 0.99, and regularization is 0.0001 [ 9 , 34 ]. Fig.…”
Clinical reports show that COVID-19 disease has impacts on the cardiovascular system in addition to the respiratory system. Available COVID-19 diagnostic methods have been shown to have limitations. In addition to current diagnostic methods such as low-sensitivity standard RT-PCR tests and expensive medical imaging devices, the development of alternative methods for the diagnosis of COVID-19 disease would be beneficial for control of the COVID-19 pandemic. Further, it is important to quickly and accurately detect abnormalities caused by COVID-19 on the cardiovascular system via ECG. In this study, the diagnosis of COVID-19 disease is proposed using a novel deep Convolutional Neural Network model by using only ECG trace images created from ECG signals of COVID-19 infected patients based on the abnormalities caused by the COVID-19 virus on the cardiovascular system. An overall classification accuracy of 98.57%, 93.20%, 96.74% and AUC value of 0.9966, 0.9771, 0.9905 is achieved for COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, COVID-19 vs. Myocardial Infarction binary classification tasks, respectively. In addition, an overall classification accuracy of 86.55% and 83.05% is achieved for COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction and Normal vs. COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction multi-classification tasks. This study is believed to have great potential to speed up the diagnosis and treatment of COVID-19 patients, saving clinicians time and facilitating the control of the pandemic.
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