Automatic Brain Tumor Detection and Classification Based on IoT and Machine Learning Techniques

Brain tumor detection, segmentation, and classification are essential in clinical diagnosis and efficient treatment. Researchers have recently shown a greater interest in attaining accurate brain tumor categorization using the Internet of Things (IoT) and machine learning. The rigidity of tumor classification and segmentation in magnetic resonance imaging is due to large data and indistinct boundaries. Hence, in this study, Machine Learning assisted Automatic Brain Tumor Detection Framework (MLABTDF) has been proposed using IoT. Our study includes establishing a deep convolutional neural network (DCNN) for spotting brain tumors from magnetic resonance imageries. This article accommodated technologies of the IoT for helping brain treatment specialists in identifying the need to make surgeries contingent on MR images. The standard medical image dataset has been gathered and experimentally examined to validate the accuracy, efficiency, specificity, sensitivity, optimum automatic recognition for non-tumor and tumor regions, and the model’s error rate utilizing statistical construction. This study pays its ability in brain irregularity recognition and analysis in the healthcare sector without humanoid intermediation. Compared to other systems, the experimental results show that the recommended MLABTDF model improves efficiency by 95.7%, segmentation and classification accuracy by 99.9%, specificity by 97.3%, sensitivity by 96.4%, optimal automatic detection by 93.4%, Matthews correlation coefficient ratio by 97.1% and error rate by 10.2%. </jats:p

High-Throughput and Power-Efficient Convolutional Neural Network Using One-Pass Processing Elements

In recent decades, convolutional neural network (CNN) has become essential in many real-time applications due to its massive computational ability. But its use in portable devices is limited due to its high computation requirements. This paper proposes a novel One-Pass Processing Element (OPPE) to mitigate this limitation. The proposed OPPE removes redundant computations by eliminating those with zeros that leads to low area as well as low power consumption. The proposed OPPE model is evaluated with the help of VGG-16-based CNN accelerator. The proposed OPPE design reduces the number of four-input LUTs by 5.19%, 15.91%, 10.06% and 4.93% and the power consumption by 4.26%, 7.36%, 5.81% and 1.55% when compared with the conventional processing element (PE), activation gating PE, weight gating PE and zero gating PE, respectively. The proposed CNN accelerator design using OPPE achieves high throughput with less resource utilization. </jats:p

Sareenet : saree texture classification via region-based patch generation with an optimized efficient Aquila network

Online saree shopping has become a popular way for adolescents to shop for fashion. Purchasing from e-commerce is a huge time-saver in this situation. Female apparel has many difficult-to-describe qualities, such as texture, form, colour, print, and length. Research involving online shopping often involves studying consumer behaviour and preferences. Fashion image analysis for product search still faces difficulties in detecting textures based on query images. To solve the above problem, a novel deep learning-based SareeNet is presented to quickly classify the tactile sensation of a saree according to the user’s query. The proposed work consists of three phases: i) saree image pre-processing phase, ii) patch generation phase, and iii) texture detection and optimization for efficient classification. The input image is first denoised using a contrast stretching adaptive bilateral (CSAB) filter. The deep learning-based mask region-based convolutional neural network (Mask R-CNN) divides the region of interest into saree patches. A deep learning-based improved EfficientNet-B3 has been introduced which includes an optimized squeeze and excitation block to categorise 25 textures of saree images. The Aquila optimizer is applied within the squeeze and excitation block of the improved EfficientNet to normalise the parameters for improving the accuracy in saree texture classification. The experimental results show that SareeNet is effective in categorising texture in saree images with 98.1% accuracy. From the experimental results, the proposed improved EfficientNet-B3 improves overall accuracy by 2.54%, 0.17%, 2.06%, 1.78%, and 0.63%, for MobileNet, DenseNet201, ResNet152, and InspectionV3, respectively