Developing improved algorithms for detection and analysis of skin cancer

University of Technology Sydney. Faculty of Engineering and Information Technology.Malignant melanoma is one of the deadliest forms of skin cancer and number of cases showed rapid increase in Europe, America, and Australia over the last few decades. Australia has one of the highest rates of skin cancer in the world, at nearly four times the rates in Canada, the US and the UK. Cancer treatment costs constitute more 7.2% of health system costs. However, a recovery rate of around 95% can be achieved if melanoma is detected at an early stage. Early diagnosis is obviously dependent upon accurate assessment by a medical practitioner. The variations of diagnosis are sufficiency large and there is a lack of detail of the test methods. This thesis investigates the methods for automated analysis of skin images to develop improved algorithms and to extend the functionality of the existing methods used in various stages of the automated diagnostic system. This in the long run can provide an alternative basis for researchers to experiment new and existing methodologies for skin cancer detection and diagnosis to help the medical practitioners. The objective is to have a detailed investigation for the requirements of automated skin cancer diagnostic systems, improve and develop relevant segmentation, feature selection and classification methods to deal with complex structures present in both dermoscopic/digital images and histopathological images. During the course of this thesis, several algorithms were developed. These algorithms were used in skin cancer diagnosis studies and some of them can also be applied in wider machine learning areas. The most important contributions of this thesis can be summarized as below: - Developing new segmentation algorithms designed specifically for skin cancer images including digital images of lesions and histopathalogical images with attention to their respective properties. The proposed algorithm uses a two-stage approach. Initially coarse segmentation of lesion area is done based on histogram analysis based orientation sensitive fuzzy C Mean clustering algorithm. The result of stage 1 is used for the initialization of a level set based algorithm developed for detecting finer differentiating details. The proposed algorithms achieved true detection rate of around 93% for external skin lesion images and around 88% for histopathological images. - Developing adaptive differential evolution based feature selection and parameter optimization algorithm. The proposed method is aimed to come up with an efficient approach to provide good accuracy for the skin cancer detection, while taking care of number of features and parameter tuning of feature selection and classification algorithm, as they all play important role in the overall analysis phase. The proposed method was also tested on 10 standard datasets for different kind of cancers and results shows improved performance for all the datasets compared to various state-of the art methods. - Proposing a parallelized knowledge based learning model which can make better use of the differentiating features along with increasing the generalization capability of the classification phase using advised support vector machine. Two classification algorithms were also developed for skin cancer data analysis, which can make use of both labelled and unlabelled data for training. First one is based on semi advised support vector machine. While the second one based on Deep Learning approach. The method of integrating the results of these two methods is also proposed. The experimental analysis showed very promising results for the appropriate diagnosis of melanoma. The classification accuracy achieved with the help of proposed algorithms was around 95% for external skin lesion classification and around 92 % for histopathalogical image analysis. Skin cancer dataset used in this thesis is obtained mainly from Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital. While for comparative analysis and benchmarking of the few algorithms some standard online cancer datasets were also used. Obtained result shows a good performance in segmentation and classification and can form the basis of more advanced computer aided diagnostic systems. While in future, the developed algorithms can also be extended for other kind of image analysis applications

Children with Febrile Seizures have Lower Zinc Levels

Objective: To find the association between zinc deficiency and febrile seizures in children of 6 months and 5 years of age. Materials and Methods: Cross-Sectional Descriptive Study was carried out at the Department of Pediatrics, Benazir Bhutto Hospital, Rawalpindi for a duration of six months (From 11th March to 31st August 2017). After taking approval of the Ethical Research Committee of Rawalpindi Medical College and taking informed consent from the parents/ guardians, children selected according to the inclusion/exclusion criteria. Patient profile including name, age, sex, address, hospital number, serial number, date of inclusion in the study was noted. Data was collected from the Patient’s charts and/or by direct interview of the child’s guardian. Using aseptic measures, 2ml of blood from venipuncture utilizing a 22-gauge antiseptic needle, in no more than 24 hours of hospital visitation was reserved. Evaluation of serum zinc was completed in no more than 6 hours of collection. The copy was then given to the lab testing and thus this report was then approved by the physician. Results: In our study, out of 145 cases, 52.41%(n=76) were between 1-3 years of age whereas 47.59% (n=69) were between 4-6 years of age, the mean and standard deviation was calculated as 3.54 + 1.50 years, 50.34% (n=73) were male whereas 49.66% (n=72) were females. Mean serum zinc levels were calculated as 64.28 + 12.13 mcg/dl. The frequency of hypozincemia in febrile seizures among children presenting at tertiary care hospitals was 54.48% (n=79). Conclusion: These analysis outcomes depicted that children with febrile seizures had notably lesser serum zinc measures

In silico mutation analysis of human beta globin gene in sickle cell disease patients

Background: Sickle cell disease is an inherited blood disorder that affects red blood cells. People with sickle cell conditions make a different form of hemoglobin a called hemoglobin S. Sickle cell conditions are inherited from parents in much the same way as blood type, hair color and texture, eye color and other physical traits. Sickle cell disease occurs due to a single mutation on the b-globin gene, namely, a substitution of glutamic acid for valine at position 6 of the b chain. Several mutations in HBB gene can cause sickle cell disease. Abnormal versions of beta-globin can distort red blood cells into a sickle shape. The sickle-shaped red blood cells die prematurely, which can lead to anemia. The study is focused on analysis of HBB gene with its different variants, Evolutionary pathways and protein domains by using various bioinformatics tools.Methods: The study is focused on analysis of HBB gene with its different variants, Evolutionary pathways and protein domains by using various bioinformatics tools.Results: Sickle cell disease occurs due to a single mutation on the b-globin gene, namely, a substitution of glutamic acid for valine at position 6 of the b chain. Several mutations in HBB gene can cause sickle cell disease. Abnormal versions of beta-globin can distort red blood cells into a sickle shape. Comparative study shown 38 different genes with little genetic variation among different species.Conclusion: Studies suggested that there is need to maintain a primary prevention program to detect sickle cell disease at earlier stages despite having a large high risk. Preventive diagnosis and follow-up would reduce infant mortality by preventing the development of severe anemia as well as dangerous complications. In short, sickle cell disease surveillance would avert loss of life, measured as the number of years lost due to ill-health, disability or early death.

Rumor Detection in Business Reviews Using Supervised Machine Learning

© 2018 IEEE. Currently, a high volume of business data is generating with a high velocity in different forms like unstructured, structured or semi-structured. Due to social media arrival, there is a deluge of business rumors and their manual screening is time-consuming and difficult. In the current social computing era, it is necessary to move towards an automated process for the detection of business rumors. This work aims at developing an automated system for detecting business rumors from online business reviews using supervised machine learning classifiers, namely Logistic Regression, Support Vector Classifier (SVC), Naïve Bayesian (NB), K-Nearest Neighbors (KNN) to classify the business reviews into rumor and nonrumor. Experimental results show that Naïve Bayesian (NB), achieved efficient results with respect to other classifiers with an accuracy of 72.43 %

Stock market trend prediction using supervised learning

© 2019 Association for Computing Machinery. The stock trend prediction has received considerable attention of researchers in recent times. It is an important application in machine learning domain. In this work, we propose a machine learning based stock trend prediction system with a focus on minimizing data sparseness in the acquired datasets. We perform outlier detection on the acquired dataset for dimensionality reduction and employ K-nearest neighbor classifier for predicting stock trend. Results obtained show the effectiveness of the proposed system, when compared with baseline studies

Impact of Brand Activism on Brand Personality and Brand Loyalty

Objective: This study sets out to deeply explore the way different aspects influence brand loyalty. We're particularly interested in understanding how customers perceive different brands when brands are doing some activities regarding social welfare, and how this participation of brands leads to loyalty. Methodology: We conducted a survey from 251 people using an online questionnaire. The results show that people like to use those brands that actively participate in socio-political activities. This thing increases the trust of customers for a brand as they think it is a good brand and enhances brand loyalty. Findings: This study helps us understand better why people decide to repurchase a brand again and again after they've purchased it once. The findings are useful for marketers who manage brands and different marketing strategies, as they can help them make strategies on how to retain customers. This also shows us that customer satisfaction and brand trust also impact the effect of brand loyalty. Implications: With this information, marketers may tailor their offerings to specific customers' preferences, creating more memorable experiences. The study provides a roadmap for marketing success

In silico mutation analysis of human beta globin gene in sickle cell disease patients

Background: Sickle cell disease is an inherited blood disorder that affects red blood cells. People with sickle cell conditions make a different form of hemoglobin a called hemoglobin S. Sickle cell conditions are inherited from parents in much the same way as blood type, hair color and texture, eye color and other physical traits. Sickle cell disease occurs due to a single mutation on the b-globin gene, namely, a substitution of glutamic acid for valine at position 6 of the b chain. Several mutations in HBB gene can cause sickle cell disease. Abnormal versions of beta-globin can distort red blood cells into a sickle shape. The sickle-shaped red blood cells die prematurely, which can lead to anemia. The study is focused on analysis of HBB gene with its different variants, Evolutionary pathways and protein domains by using various bioinformatics tools.Methods: The study is focused on analysis of HBB gene with its different variants, Evolutionary pathways and protein domains by using various bioinformatics tools.Results: Sickle cell disease occurs due to a single mutation on the b-globin gene, namely, a substitution of glutamic acid for valine at position 6 of the b chain. Several mutations in HBB gene can cause sickle cell disease. Abnormal versions of beta-globin can distort red blood cells into a sickle shape. Comparative study shown 38 different genes with little genetic variation among different species.Conclusion: Studies suggested that there is need to maintain a primary prevention program to detect sickle cell disease at earlier stages despite having a large high risk. Preventive diagnosis and follow-up would reduce infant mortality by preventing the development of severe anemia as well as dangerous complications. In short, sickle cell disease surveillance would avert loss of life, measured as the number of years lost due to ill-health, disability or early death.