A concatenation of deep and texture features for medicinal trash image classification using EnSegNet-DNN-based transfer learning

Objective: To maximize the accuracy of classifying the medical wastage, an Enhanced Segmentation Network (EnSegNet) with Deep Neural Network-Trash Classification (EnSegNet-DNN-TC) is proposed in this article. Methods: Initially, a core trainable segmentation network called SegNet framework is proposed which uses the Encoder-Decoder Network (EDN) and a pixel-wise classification layer for image segmentation. The decoder is used to upsample its low-resolution input feature maps via max-pooling. Also, SegNet uses fewer parameters for training. The uncertainty inherent to the EDN is modeled by the Bayesian functions to segment the input images. But, this SegNet can sample a limited amount of pixels in the images. Hence, an EnSegNet is proposed that uses Content-Sensitive Sampling (CSS) to sample more pixels in the data-sparse regions and fewer pixels in data-dense regions. Once the segmentation is completed, the DNN is applied for classifying the wastage using the segmented images. Findings: The experimental results show that the EnSegNet-DNN-TC framework achieves 88% accuracy compared to the DNN-TC for considering 100 images of different categories of biomedical wastes from the trash image dataset.
Keywords: Biomedical wastage classification; deep learning; image segmentation; ResNext; encoder-decoder network

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Enhanced segmentation network with deep learning for Biomedical waste classification

The Internet of Things (IoT) is being used to create new applications for smart cities. Waste management is one issue that requires various IoT components for assistance, such as RFIDs and sensors. An efficient and innovative waste collection system is required to minimize investment, operational, and expenditure costs. In this paper, the novel idea is to develop an intelligent waste management model for smart cities using a hybrid genetic algorithm (GA)–fuzzy inference engine. The system can read, collect, and process information intelligently using a fuzzy inference engine that decides dynamically how to manage a waste collection. The aim of this model is to enhance its correctness and robustness, primarily, in addition to reducing errors that arise due to working conditions. GA is used for optimization to determine the best combination of rules for the fuzzy inference system (FIS). A Mamdani model is used to estimate waste management. The proposed model uses sensors to collect vital information, and FIS is trained using fuzzy logic to determine the probability that the smart bin is nearly full. The primary issue with the traditional genetic algorithm is that during the execution of the algorithm, there is a possibility of essential gene loss. The essential gene loss refers to information relevant to location, details regarding waste filling parameters, etc., which may lead to efficiency or accuracy loss. This problem is overcome by integrating fuzzy logic with a genetic algorithm to identify crucial genes by preserving the FIS interpretability. Our system uses cost-effective, small-size sensors and ensures this solution is reproducible. The Proteus simulator is used for experiments, and satisfactory results are obtained. Overall accuracy, precision, and recall of 95.44%, 96.68%, and 93.96% are obtained in the proposed model. Classification of recyclable items is also performed, and accuracy is determined for every item, resulting in the minimization of resource waste. The cost of manual interpretation is minimized in the intelligent smart waste management system in comparison to the traditional approach, as shown in the experiments.

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An Intelligent Waste Management Application Using IoT and a Genetic Algorithm–Fuzzy Inference System

Abstract The aim of this review article is planning to collect, transport, processing and dispose of hazardous and non-hazardous biomedical waste, with a special concern on the biomedical waste tracking system in India. This review article sheds insight on some of the tracking systems of COVID-19 waste over the barcode, IoT, and GPS-based trash monitoring, related to COVID-19 waste management. Better waste management may reduce the amount of time that garbage is exposed to the environment and the risk of transportation. COVID-19 waste treatment facility might assist to reduce the risk of coronavirus transmission, as well as the hazardous component risk respectively.

A Review Study to Investigate the Tracking of Biomedical Waste during Covid-19 Pandemic in India

Context:

Medical waste is a grave problem for health care staff, public health and ecosystem. Due to inappropriate handling and disposing of sharp waste after surgery there is an increase in spread infectious diseases and hence a threat to human life. 

AIM and OBJECTIVES: 
To determine the knowledge regarding needle-stick and sharp object injury, practices regarding disposal of used sharp instruments and attitude towards sharp instrument waste management amongst auxillary staff. 

MATERIALS AND METHODS:
It was descriptive analytical study. Ethical clearance was taken from institutional review board. Informed consent was taken from the participants. 275 subjects (nurses and auxillary staff) were given a questionnaire which consisted of 15 sets of questions based on knowledge, attitude and practices of disposing and handling sharp objects after surgery. As per the answers, scores were given as Weak <50%, Moderate 50 – 74.99%, Good 75 to 100%. 

Results: Statistical analysis was done using chi square test. Study showed that the knowledge and awareness as well as practice protocol adopted were moderate (50-75%) while their attitude towards handling and disposing sharp objects was definitely good. 

Conclusion: There is dearth of knowledge about sharp waste management and also practice protocol followed have to be improved. 

Keywords: biomedical waste, auxillary staff, ecosystem, health hazards, sharp objects.

Key message: There is a void in the knowledge about sharp waste handling and management and the practices followed have to be upgraded and this lacunae can be filled by introducing more continuing education and training programmes.

Determination of the knowledge attitude and practices of handling and disposing sharp objects after surgery amongst auxillary staff.

Background: We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical Waste (BMW) for garbage disposal and management Methods: The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise in the image After that, the histogram features of the filtered image were extracted with the help of proposed Modified Local Tetra Pattern (MLTrP) technique Finally, the Relevance Vector Machine (RVM) was used to classify the BMW into human body parts, plastics, cotton and liquids Results: The BMW image was collected from the garbage image dataset for analysis The performance of the proposed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate and accuracy with the help of MATLAB When compared to the existing techniques, the proposed techniques provided the better results Conclusion: This work proposes a new texture analysis and classification technique for BMW management and disposal It can be used in many real time applications such as hospital and healthcare management systems for proper BMW disposal

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Vasumathi Ayyallu Madangopal

Papers

A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm