Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model

Peer reviewe

Mapping Change in Spatial Extent and Density of Mangrove Forest at Karachi Coast Using Object Based Image Analysis

Karachi shoreline is more than 135 Km long significant for marine fishery breeding and spawning. During 2005 to 2018 the mangrove forest areas in Karachi increased in extent but declined in density. The main cause of mangrove cover change in this region are coastal region development (port building, industrial area and waterfront project). This study aims to monitor both extent and density changes of mangrove forest at Karachi coast. For this purpose, the Landsat imagery was used of the years 2005 and 2018 covering a span of 14 years. The imageries were processed through Normalized Difference Vegetation Index (NDVI) analysis. Simultaneously, random sample locations were identified for mapping and validation of mangrove forest extent and density during 2005 to 2018. The sample locations were categorized as dense, normal and sparse classes. In the next step, sample locations were plotted on NDVI images to determine mean, minimum and maximum values for each class of mangrove forest. In the final step, the accuracy assessment was done using Kappa statistics. Results show that overall accuracy of 2018 imagery is better than 2005 Landsat imagery. The overall extent of mangrove forest increased in the past years

Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of nois

Infekcija vrstom Ornithobacterium rhinotracheale u crvenolikih vivaka (Vanellus indicus) u Pakistanu - prikaz slučaja

Respiratory infections are of major concern in the poultry industry in Pakistan. Previously, wild birds have been reported to transmit respiratory infections. The Red Wattled Lapwing (RWL) is a wild bird prevalent in the Indus basin and the wetlands of Punjab, Pakistan. Out of total of eighteen RWL birds housed at Lahore Zoo, Pakistan, three birds died after showing signs of respiratory distress and paralysis, in August, 2014. Postmortem examination revealed air sacculitis and pneumonia. Microbiological examination revealed Ornithobacterium rhinotracheale (ORT) as the causative agent, which was later confirmed by Polymerase Chain Reaction (PCR). The isolate was found to be susceptible to amoxicillin, erythromycin, tetracycline and enrofloxacin, and resistant to gentamycin, neomycin and sulfamethoxazole/trimethoprim. All the remaining birds were treated with long acting tetracycline, and diseased birds eventually recovered. No further mortality was declared. This is the first report of its kind which demonstrates ORT infection in RWL in Punjab, Pakistan.Dišne infekcije od velike su važnosti za peradarsku industriju u Pakistanu. Znano je da ih mogu prenositi divlje ptice. Crvenoliki vivak nastanjuje bazen Indus i močvarna područja Pendžaba u Pakistanu. Od ukupno 18 crvenolikih vivaka iz Zoološkog vrta Lahore, tri su uginula nakon pojave znakova dišnog poremećaja i paralize u kolovozu 2014. Razudbom je utvrđen sacculitis i pneumonija. Mikrobiološkom pretragom dokazan je Ornithobacterium rhinotracheale što je bilo potvrđeno lančanom reakcijom polimerazom. Izolat je bio osjetljiv na amoksicilin, eritromicin, tetraciklin i enrofloksacin, a otporan na gentamicin, neomicin i sulfametoksazol/trimetoprim. Sve preživjele ptice bile su liječene tetraciklinom s produženim djelovanjem i ozdravile. Novi slučajevi uginuća nisu bili primijećeni. Ovo je prvo izvješće o pojavi infekcije vrstom Ornithobacterium rhinotracheale u crvenolikog vivka u Pendžabu u Pakistan

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model

A brain tumor is an accumulation of malignant cells that results from unrestrained cell division. Tumors can result in crucial effects if they are not promptly and accurately recognized. Misdiagnosis can result in ineffective therapy, which decreases the patient's survival rate. The standard procedure for determining the presence of brain tumors and the type of tumors is magnetic resonance imaging (MRI). But as technology advances, it gets harder to comprehend huge amounts of data generated in an acceptable time. However, building a deep learning model from the start requires collecting enormous amounts of labeled data, which is a costly, time-consuming operation. A method to solve these issues is transfer learning of a deep learning model that has already been trained on the ImageNet dataset. In this research, the classification of brain tumors using several pre-trained deep learning models, i.e., different variations of ResNet, VGG, and DenseNet models, are being trained on a brain tumor dataset and compared. According to experiments, the ResNet50 model with a fine-tuned and transfer learning approach has achieved the highest training accuracy of 99%, validation accuracy of 96%, and test accuracy of 80%.

Unpacking the relationship between technological conflicts, dissatisfaction, and social media discontinuance intention: An integrated theoretical perspective

Previous research has largely overlooked the examination of the association between family, work, and personal (FWP) conflict, user dissatisfaction, and subsequent discontinuation intention in the context of social networking sites (SNS). Addressing this research gap, the present study aims to present an integrated theoretical perspective that combines Expectancy Disconfirmation Theory (EDT) and Merton's functions. By doing so, we seek to provide a comprehensive understanding of the factors influencing SNS withdrawal behavior. To achieve this objective, data were collected from 360 SNS users using a time-lag method across three waves, and Structural Equation Modeling (SEM) was employed for data analysis. The findings of our study reveal that all three disconfirmation-based factors (i.e., FWP conflicts) positively contribute to SNS user dissatisfaction, which subsequently leads to users' intention to discontinue their SNS usage. Additionally, we explored the moderating role of Merton's functions, specifically manifest and latent functions, in influencing users' decisions to discontinue SNS use. The results indicate that the manifest functions of social media weaken the relationship between dissatisfaction and discontinuation intention, whereas the latent functions do not exhibit a significant interaction effect. By proposing a dual theoretically integrated mechanism of SNS discontinuation intention, study contributes to the existing literature in the field of information systems. Furthermore, our findings provide valuable insights for managers regarding the timing and manner in which social media FWP conflicts can lead to user dissatisfaction. This knowledge can assist in the development of effective strategies aimed at retaining users in SNS and enhancing their overall user experience

Determinants of Academic Achievement at Higher Education

The purpose of the present study was to explore the determinants of students’ academic achievement as well as gender based differences of these determinants in social and natural sciences. We selected 807 students (male = 504, female = 303) from Bahauddin Zakariya University by using multi-stage stratified random sampling and the data was collected through self-administered questionnaire whereas parenting style was measure by Lamborn, et al. (1991) questionnaire. Difference of teacher behavior toward male and female students in social and natural sciences was identified. It was explored that teachers’ politeness and time spend on studies by students have significant effect on academic achievement. The study suggested that teachers should be unbiased regarding gender during teaching and students should be more time devoted to their studies