A Novel Convolutional Neural Network Based on Combined Features from Different Transformations for Brain Tumor Diagnosis

Brain tumors are a leading cause of death worldwide. With the advancements in medicine and deep learning technologies, the dependency on manual classification-based diagnosis drives down owing to their inaccurate diagnosis and prognosis. Accordingly, the proposed model provides an accurate multi-class classification model for brain tumor using the convolution neural network (CNN) as a backbone. Our novel model is based on concatenating the extracted features from the proposed three branches of CNN, where each branch is fed by the output of different transform domains of the original magnetic resonance image (MRI). These transformations include Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and the time-domain of the original image. Then, the CNN is employed followed by a concatenation layer, flatten laver, and dense layer, before using the SoftMax layer. The proposed model was applied to the Figshare dataset of brain tumor which consists of three classes pituitary, glioma, and meningioma. The results proved the advantage of the proposed system which achieved a high mean performance over 5-fold cross-validation with 98.89% accuracy, 98.78% F1-score, 98.74% precision, 98.82% recall, and 99.44% specificity. The comparative study with well-known models, as well as the pre-trained CNN models, established the potential of the proposed model. This novel approach has the potential to significantly improve brain tumor classification accuracy. It enables a more comprehensive and objective analysis of brain tumors, leading to improved treatment decisions and better patient care

A Novel Convolutional Neural Network Based on Combined Features from Different Transformations for Brain Tumor Diagnosis

Brain tumors are a leading cause of death worldwide. With the advancements in medicine and deep learning technologies, the dependency on manual classification-based diagnosis drives down owing to their inaccurate diagnosis and prognosis. Accordingly, the proposed model provides an accurate multi-class classification model for brain tumor using the convolution neural network (CNN) as a backbone. Our novel model is based on concatenating the extracted features from the proposed three branches of CNN, where each branch is fed by the output of different transform domains of the original magnetic resonance image (MRI). These transformations include Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and the time-domain of the original image. Then, the CNN is employed followed by a concatenation layer, flatten laver, and dense layer, before using the SoftMax layer. The proposed model was applied to the Figshare dataset of brain tumor which consists of three classes pituitary, glioma, and meningioma. The results proved the advantage of the proposed system which achieved a high mean performance over 5-fold cross-validation with 98.89% accuracy, 98.78% F1-score, 98.74% precision, 98.82% recall, and 99.44% specificity. The comparative study with well-known models, as well as the pre-trained CNN models, established the potential of the proposed model. This novel approach has the potential to significantly improve brain tumor classification accuracy. It enables a more comprehensive and objective analysis of brain tumors, leading to improved treatment decisions and better patient care

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

An Ensemble Based Classification Approach for Medical Images

Ensemble classification is a classifier applied to improve the performance of the single classifiers by fusing the output of the individual classifier models. Research in ensemble methods has largely revolved around designing ensemble consisting of single classifier models. The main discovery of the ensemble classifier, constructed by ensemble machine algorithms is to perform much better accuracy than the single classifiers. The ability to perform classification accuracy in single classifier models has been increased but in single classifier accuracy of classification is less. The difficulty arises because the algorithms for single classifier algorithm have designed with less capacitance. Now-a-days more researchers are applying the ensemble learning algorithm for classification to obtain high accuracy in an effectual manner