RESUMEN
OBJECTIVES: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast. METHODS: This research further configures a capsule network (CapsNet) for brain tumor classification. Results of the latest deep neural network (NN) architectures for tumor detection are compared and presented. The VGG16 and CapsNet architectures yield the highest f1-score and precision values, followed by VGG19. Overall, ResNet152, MobileNet, and MobileNetV2 give us the lowest f1-score. RESULTS: The VGG16 and CapsNet have produced outstanding results. However, VGG16 and VGG19 are more profound architecture, resulting in slower computation speed. The research then recommends the latest suitable NN for effective brain tumor detection. CONCLUSIONS: Finally, the work concludes with future directions and potential new architectures for tumor detection.
RESUMEN
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
RESUMEN
Human tooth functionality is the most important for the human body to become fit and healthy. Due to the disease attacks in human teeth, parts may lead to different fatal diseases. A spectroscopy-based photonic crystal fiber (PCF) sensor was simulated and numerically analyzed for the detection of dental disorders in the human body. In this sensor structure, SF11 is used as the base material, gold (Au) is used as the plasmonic material, and TiO2 is used within the gold and sensing analyte layer, and the sensing medium for the analysis of the teeth parts is the aqueous solution. The maximum optical parameter values for the human tooth parts enamel, dentine, and cementum in terms of wavelength sensitivity and confinement loss were obtained as 28,948.69 nm/RIU and 0.00015 dB/m for enamel, 33,684.99 nm/RIU and 0.00028 dB/m, and 38,396.56 nm/RIU and 0.00087 dB/m, respectively. The sensor is more precisely defined by these high responses. The PCF-based sensor for tooth disorder detection is a relatively recent development. Due to its design flexibility, robustness, and wide bandwidth, its application area has been spreading out. The offered sensor can be used in the biological sensing area to identify problems with human teeth.
RESUMEN
Open Educational Resources (OER) are teaching, and research resources provided under the Creative Commons (CC) licenses and can be freely used, shared, and modified. However, OER adoption is not widespread, and various barriers remain in the way of its growing emphasis. This article is aimed to investigate OER adoption in higher institutions by using Rogers' Diffusion of Innovation (DOI) theory. 422 responses to an online survey from faculty are gathered and analyzed, where adaptive attributes of DOI are adopted. The results of the descriptive method confirmed that relative advantage has a positive impact on faculty OER adoption. Indeed, positive impacts of observability and complexity are also shown. Ultimately, the findings from the structural model used, indicated that there is a positive correlation between trialability and respectively complexity and compatibility. Whereas relative advantage of OER impacts positively complexity and negatively compatibility. This study showed that it is not enough that faculty agree on OER benefits for teaching and research, the OER adoption rate must increase. Decision-makers in higher institutions are asked to perform additional OER initiatives to overcome challenges related to OER trialability, complexity, and compatibility.
