Computer Aided Dysplasia Grading for Barrett’s Oesophagus Virtual Slides

Dysplasia grading in Barrett’s Oesophagus has been an issue among pathologist worldwide. Despite of the increasing number of sufferers every year especially for westerners, dysplasia in Barrett’s Oesophagus can only be graded by a trained pathologist with visual examination. Therefore, we present our work on extracting textural and spatial features from the tissue regions. Our first approach is to extract only the epithelial layer of the tissue, based on the grading rules by pathologists. This is carried out by extracting sub images of a certain window size along the tissue epithelial layer. The textural features of these sub images were used to grade regions into dysplasia or not-dysplasia and we have achieved 82.5% AP with 0.82 precision and 0.86 recall value. Therefore, we have managed to overcame the ‘boundary-effect’ issues that have usually been avoided by selecting or cropping tissue image without the boundary. Secondly, the textural and spatial features of the whole tissue in the region were investigated. Experiments were carried out using Grey Level Co-occurrence Matrices at the pixel-level with a brute-force approach experiment, to cluster patches based on its texture similarities.Then, we have developed a texture-mapping technique that translates the spatial arrangement of tissue texture within a tissue region on the patch-level. As a result, three binary decision tree models were developed from the texture-mapping image, to grade each annotated regions into dysplasia Grade 1, Grade 3 and Grade 5 with 87.5%, 75.0% and 81.3% accuracy percentage with kappa score 0.75, 0.5 and 0.63 respectively. A binary decision tree was then used on the spatial arrangement of the tissue texture types with respect to the epithelial layer to help grade the regions. 75.0%, 68.8% and 68.8% accuracy percentage with kappa value of 0.5, 0.37 and 0.37 were achieved respectively for dysplasia Grade 1, Grade 3 and Grade 5. Based on the result achieved, we can conclude that the spatial information of tissue texture types with regards to the epithelial layer, is not as strong as is on the whole region. The binary decision tree grading models were applied on the broader tissue area; the whole virtual pathology slides itself. The consensus grading for each tissue is calculated with positivity table and scoring method. Finally, we present our own thresholded frequency method to grade virtual slides based on frequency of grading occurrence; and the result were compared to the pathologist’s grading. High agreement score with 0.80 KV was achieved and this is a massive improvement compared to a simple frequency scoring, which is only 0.47 KV

A systematic literature review of cloud computing adoption and impacts among Small Medium Enterprises (SMEs)

Although cloud computing is one of the most significant trends in information technology acquisition today, its adoption amongst the SMEs is still behind the larger conterparts. Additionally, among those that use, many face challenges to gain benefits as what is normally claimed. More research is needed to understand the issue. The purpose of this paper is to present the findings of a Systematic Literature Review (SLR) conducted related to cloud computing adoption among SMEs, particularly focusing on the post adoption stage. SLR method was employed as this method enable the review been done in a more comprehensive and rigorous manner. A total of 39 relevant articles were reviewed and the findings indicate that most past researches on cloud computing and SMEs focused on adoption, exploring factors that affect the adoption. Very few studies looked at the post adoption stage or the impacts of cloud computing on SMEs

A hierarchical classifier for multiclass prostate histopathology image gleason grading

Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4).To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and one- versus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed.However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the ‘divide-and-conquer’ principle by allocating each binary task into two separate subtasks; strong and weak, based on the power of the samples in each binary task. Conversely, the strong samples include more information about the considered task, which motivates the production of the final prediction. Experimental results on prostate histopathological images illustrated that the MLA significantly outperforms the Ovall and OVO approaches when applied to the ensemble framework.The results also confirmed the high efficiency of the ensemble framework with the MLA scheme in dealing with the multiclass classification problem

Pengelasan sebutan huruf hijaiyah menggunakan teknik pembelajaran mesin

Fitur Mel-frequency cepstral coefficients (MFCC) dan teknik pengelasan berdasarkan pembelajaran mesin sering digunakan dalam mengelaskan sebutan huruf-huruf hijaiyah. Walaupun begitu, berdasarkan kajian-kajian lepas, prestasi ketepatan pengelasan sebutan huruf hijaiyah masih lagi rendah walaupun dengan penggunaan algoritma pembelajaran mesin dan fitur MFCC. Oleh itu, kajian khas untuk menganalisis fitur dan teknik pembelajaran mesin yang sesuai akan dibincangkan dalam kertas kajian ini. Selain itu, bilangan huruf hijaiyah juga ditingkatkan kepada 30 huruf mengikut resam uthmani. Kajian ini mahu membuktikan bahawa penggunaan fitur dan teknik pengelasan yang sesuai mampu mengelaskan sebutan huruf hijaiyah dan memberikan prestasi ketepatan yang tinggi walaupun dengan jumlah huruf yang banyak. Kajian ini dilakukan berdasarkan kepada enam fasa utama dalam metodologi kajian ini termasuklah pemprosesan isyarat, penyarian fitur, pemprosesan dan pemilihan fitur, pengelasan dan akhir sekali pengujian, penilaian dan analisis. Kadar persampelan yang digunakan bagi kesemua modul pemprosesan isyarat pertuturan dalam kajian ini adalah 44.1 kHz. Dapatan kajian menunjukkan fitur MFCC merupakan fitur paling sesuai bagi mengelaskan sebutan huruf hijaiyah berbanding fitur-fitur lain yang telah diekstrak berdasarkan kepada ‘rank’ dalam hasil pemilihan fitur. Perbandingan prestasi ketepatan menunjukkan teknik pengelasan Random Forest (RF) mencapai ketepatan yang tinggi dengan menggunakan fitur MFCC, iaitu purata sebanyak 97~99% bagi setiap huruf hijaiyah berbanding teknik pengelasan lain yang telah diuji dalam kajian ini. Kesimpulannya, penggunaan fitur MFCC dan teknik pengelasan RF mampu memberikan prestasi ketepatan pengelasan sebutan huruf hijaiyah yang tinggi sekaligus meningkatkan prestasi pengelasan sebutan huruf hijaiyah kajian lepas, sehingga 98.29% secara purata untuk 30 huruf

A model to assess the impacts of cloud computing use on SME performance: a resource-based view

Currently, cloud computing services that are available in the market are very appealing to SMEs. This is because these services are not only up-to-date in terms of ICT but more importantly they are affordable to them. However, the adoption of the cloud computing services presents significant challenges to the SMEs. They need to determine the appropriate path in order to ensure their sustainable presence in the cloud computing environment. Additionally, SMEs have to assess whether cloud services can provide value to their business. Past research has focused on cloud computing adoption by SMEs; the post-adoption and impacts of cloud computing was not thoroughly investigated. Using the Resource-Based theory, this paper discusses a conceptual model developed to assess the impact of cloud computing on SME performance, mediated by cloud computing benefits. The role of environmental turbulence is also included in the model which is postulated to moderate the effect of cloud infrastructure capability on cloud computing benefits. The model contributes towards understanding the impacts of cloud computing on SMEs from the lens of Resource-based View, and practically can guide SME managers in planning their cloud migration initiative

Dr. LADA: diagnosing black pepper pest and diseases with decision tree

Malaysia has the distinction of being the world’s fifth largest pepper producer country whereby 98% of the country's annual production comes from the State of Sarawak. However, crop loss due to pest and disease incidence has been identified as one of the major pepper production constraints. Inefficient advisory mechanism and assistance from extension staff due to technical and logistic limitations have hindered the pest and disease diagnosis effort for pepper. Currently, extension staff from MPB will have to travel to the rural farms when contacted, or during their visits to advice or treat the plants. Therefore, “DR. LADA”, was jointly developed by Malaysian Pepper Board and Universiti Kebangsaan Malaysia to diagnose six pests and ten diseases of pepper which commonly found in Malaysia and recommends appropriate management measures to solve the problems. This an interactive android-based mobile app used an inference engine utilises the forward-backward chaining methods to trigger the correct output from decision tree that inter-relates the expert rules which extracted and validated by Malaysian Pepper Board experts. Dr. LADA is a native mobile app develop on a java-based platform which provides fast performance, high degree of reliability and can be used without any internet connection. The app has been tested with 10 case studies carried out by Malaysian Pepper Board and scored 97% of accuracy. Having Dr. LADA, user can identify problems by answering a series of questions from symptoms shown by several plant parts. Therefore, the dependency of farmers on extension staff are reduced, and indirectly minimizing the extension activity costs

High CD4+/CD8+ Intratumour Ratio is associated with favourable outcome in triple-negative Breast Cancer

Triple-negative breast cancer (TNBC) is a heterogenous breast cancer subtype which accounts for 10-15% among all diagnosed breast cancers. There is increased resistance of TNBC to conventional chemotherapy and hormonal therapy due to lack of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression. Mutual relationship and interactions between both CD4+ and CD8+ T lymphocytes are very crucial for eliciting adaptive immune system during anti-cancer immune response. This study aimed to determine the ratio of CD4+ and CD8+ T lymphocytes in TNBC by immunohistochemistry assay and to investigate the association of CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs) with survival outcome. Quantification of both immunostaining TILs subset was done using conventional light microscope. A wide range of CD4+ and CD8+ TILs subset was found within intratumor stroma of TNBC with population mean score of 0.93 and 0.53, respectively. However, the difference of mean population between both TILs subsets was insignificant overall (P-value of CD4+= 0.484; CD8+= 0.835) when compared statistically using independent t-test. The ratio of CD4+/CD8+ in intratumor stroma ranged from 0.50-5.88 among all TNBC subtypes. The high CD4+/CD8+ ratio within intratumor stroma showed favorable association with survival outcome during the 2 years’ follow-up

Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system