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A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

In complex data with high dimensions, the dimension reduction methods are used to increase accuracy and speed in the classification algorithms. Feature clustering methods have had a good performance in the selection of important features of data due to using clustering methods. The process of selecting important features of data is a challenge in feature clustering methods which has led to the creation of different algorithms with different performances. The combination of the clustering methods and metaheuristic algorithms, especially the kind of population-based algorithms, have had good results in most cases. In this paper, a new feature clustering method is proposed which is used as a dimension reduction in the classification of brain tumors in 900 magnetic resonance images (MRI). The classification algorithm includes three main steps: in the first step, the Google-Net and ResNet-18 methods have been used for feature extraction of MRI images. Due to the creation of many features using the Google-Net and ResNet-18 methods, a new proposed feature clustering is introduced to reduce the feature dimensions in the second step. In designing the feature clustering algorithm, a new metaheuristic algorithm is introduced which is called the crocodiles hunting strategy optimization algorithm (CHS) that simulates crocodiles’ behavior in hunting. Also, the feature clustering algorithm introduced the new chromosome encoding for feature clustering which is called feature clustering based on the crocodiles hunting strategy optimization algorithm (FC-CHS). Finally, in the third step, the support vector machine (SVM) algorithm is used for classification. According to the results of classification on the MRI images, the proposed algorithm has achieved high accuracy in Google-Net and ResNet features based on confusion matrices. For comparing the performance of the FC-CHS, this algorithm is compared with five well-known dimension reduction algorithms. Also, real data are used to further investigate the performance of the FC-CHS algorithm. The results show that the combination of the FC-CHS and SVM algorithms have been reached high accuracy in Iris, and Wine data, and in other real data, the proposed algorithm is outperformed compared to other dimension reduction methods in most cases.