School improvement - innovative practices and experiences

This paper reflects field experiences in setting up and running the Academic Cell and the Model Teachers Resource Centre. The paper is based on work done in April 2001 up to August 2003. As a Professional Development Teacher (PDT), school improvement has been the core objective for me. This paper points out to the contributions made for the purpose of school improvement and curriculum enrichment through material development, , which focuses on developing two main areas: firstly, a Guide for Low Cost Teaching Aids and secondly, textbooks and corresponding Teacher’s Guides. Also, it discusses the procedures that were adopted in setting up a model Teacher’s Resource Centre. The fact that this centre functioned as backup for the Academic Cell’s activities and also as a source of facilitation for district based Teacher’s Resource Centres has also been elaborated on. It was found that material developing is a lengthy and complex process and requires detailed knowledge of a particular subject and the ability to clearly and effectively communicate with an audience. There are certain criteria that need to be considered in developing textbooks such as: 1) Textbooks must address the objectives of the National Curriculum. 2) The content and language should be appropriate enough in making a smooth transition from the previous level to the one that is aimed at. 3) The content must provide to students with room for creativity. 4) The presentation has to be engaging and interesting. For this purpose, an appropriate ratio of text and illustration needs to be considered. Gender balance and cultural appropriateness must also be addressed. As part of my responsibilities I was also involved with the development of Indicators for Monitoring and Supporting Schools. It is hoped that this document will play a significant role in making judgments based on evidence whether schools are progressing or not. I also worked towards setting standards for quality education in colleges through teaching, arranging workshops, making lists of teaching and learning material plus library books all of which play a vital role in achieving that purpose. I learnt that within their scope of work, Professional Development Teachers can make a big difference by providing opportunities for school improvement. In doing this however the system needed to be thoroughly understood and a personal commitment also counted

3-Methyl-1H-pyrrolo[2,1-c][1,4]oxazin-1-one

In the title mol­ecule, C8H7NO2, all the non-H atoms lie essentially in the same plane (r.m.s. deviation = 0.019 Å) In the crystal structure, weak inter­molecular C—H⋯O inter­actions link mol­ecules into chains along [100]. In addition, there are π–π stacking inter­actions between mol­ecules related by the c-glide plane, with alternating centroid–centroid distances of 3.434 (2) and 3.639 (2) Å

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases

Machine vision-based Statistical texture analysis techniques for characterization of liver tissues using CT images

Objective: To characterize human liver tissues by demonstrating the ability of machine vision, and to propose a new auto-generated report based on texture analysis that may work with co-occurrence matrix statistics. Method: The retrospective study was conducted at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, and comprised clinically verified computed tomography imaging data between October 2018 and September 2020. The image samples and related data were used to segregate classes 1-4. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues using supervised learning methods, principal component analysis, linear discriminant analysis, and non-linear discriminant analysis. Robust and reliable texture features were investigated by generating testing classes. Overall performance of the presented machine vision approach was analyzed using four parameters; precision, recall/sensitivity, F1-score, and accuracy. Statistical analysis was done using B11 software. Results: There were 312 image samples from 71 patients; 51(71.8%) males and 20(28.2%) females. Among the patients, 19(26.7%) had abscess, 15(21.1%) had metastatic disease, 23(32.4%) had tumour necrosis, 6(8.5%) had vascular disorder, and 8(11.3%) were normal. Principal component analysis, linear discriminant analysis, and non-linear discriminant analysis showed high >97.86% values, but the discrimination rate was 100% for class 4. Conclusion: Abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques using second-order statistics that may assist the radiologist and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases. Key Words: Liver abscess, Computed tomography imaging, Liver diseases, Image processing