PENGGUNAAN METODE EUCLIDEAN DISTANCE PADA CASE BASE REASONING UNTUK DIAGNOSIS DIABETES MELLITUS

Jenis kecerdasan buatan bagian dari sistem pakar yaitu Case Based Reasoning (CBR) yaitu metode membandingkan nilai kesamaan (similarity) antara data kasus baru dan data kasus yang tersimpan dalam basis kasus. Data pada pasien diabetes mellitus dapat digunakan kembali untuk dijadikan basis kasus dengan menggunakan beberapa atribut seperti identitas pasien, gejala yang dialami dan hasil tes gula darah.  Perhitungan similarity dapat dilakukan dengan mencocokan data kasus dan data kasus baru  menggunakan metode euclidean distance merupakan salah satunya. Untuk mengecek ke akuratan data yang digunakan pada casebase  yang dibandingkan dengan data yang ada kasus yang baru bias memakai  pengujian K fold -cross validation. K fold -cross validation akan menghilangkan bias pada data pada pengujian kali ini datakasus yang ada akan dibagi menjadi beberapa fold yang dipilih secara acak. Penelitian kali ini data akan dibagi menjadi 2, 3,5,7,10 dan 12 fold memperoleh nilai akurasi rata -  rata sebesar 79.71% , 83.24%, 91.63%, 91.13%, 91.11% dan 91.14

PENERAPAN CASE BASED REASONING UNTUK DIAGNOSIS DIABETES MELLITUS

Diabetes Mellitus merupakan penyakit metabolisme yang ditandai dengan meningkatnya kadar gula darah (glukosa) seseorang di dalam tubuh yang tingginya melebihi batas normal (hyperglikemia). Dalam jangka panjang penyakit diabetes mellitus dapat menyebabkan  gangguan fungsi dan kerusakan organ tubuh. Oleh sebab itu diagnosis terhadap penderita diabetes mellitus harus dilakukan secara cepat supaya komplikasi dapat diperlambat.Kasus -  kasus yang dimiliki oleh pasien diabetes mellitus bisa digunakan kembali untuk mendiagnosis kasus baru. Case Based Reasoning (CBR) merupakan sistem penalaran komputer yang menggunakan pengetahuan lama untuk mengatasi masalah baru. CBR memberikan solusi terhadap permasalahan baru dengan melihat kasus lama yang paling mendekati permasalahan baru. Sistem yang dibagun dalam penelitian ini adalah sistem CBR untuk melakukan diagnosis penyakit diabetes mellitus. Proses diagnosisnya dilakukan dengan cara memasukan permasalahan baru (target case) untuk dibandingkan dengan kasus lama (source case) untuk dihitung nilai similaritasnya. Dalam penelitian ini proses similaritasnya menggunakan metode nearest neighbour.Pengujian dilakukan dengan menggunakan 117 kasus dengan 85 kasus yang disimpan di basis kasus dan 32 data kasus yang dijadikan sebagai kasus baru. Hasil pengujian sistem dengan menggunakan data rekam medik pasien dengan diagnosis yang tervalidasi pakar menunjukkan bahwa sistem mampu mengenali tiga jenis penyakit diabetes mellitus dengan tingkat akurasi sebesar 94,29%

AVALIAÇÃO DE TÉCNICAS DE SEGMENTAÇÃO PARA LEUCÓCITOS EM IMAGENS DE SANGUE

A leucemia é um tipo de câncer que se origina na medula óssea e é caracterizado pela proliferação anormal de leucócitos no sangue. Para que ocorra a identificação correta dos linfoblastos, os especialistas examinam cada lâmina de sangue do paciente. Este método é influenciado por fatores como a experiência do hematologista e uma grande quantidade de trabalho por analisar imagem por imagem, isso pode resultar em relatórios não padronizados e até erros. Uma solução de baixo custo e eficiente é a utilização de sistema que examine as imagens microscópicas de sangue. Concluiu-se a partir da revisão literária que o processo de automação desse sistema depende de uma segmentação adequada. Neste trabalho, comparamos 9 métodos de segmentação em três bases de imagens públicas com o objetivo de verificar os erros nos métodos a fim de determinar qual deles apresenta os melhores resultados

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm subimages are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method

Automatic detection for acute lymphoid leukemia images based non local region approach

Orientador: Prof. Dr. Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 03/09/2018Inclui referências: p.90-95Resumo: A leucemia linfoide aguda (LLA) é o tipo de câncer mais comum a se manifestar na infância, apesar de apresentar rápida evolução em seu quadro clínico a LLA possui relativamente baixa mortalidade quando identificada e tratada em seu estágio inicial. Como o diagnóstico de LLA é feito por médicos hematologistas com base na análise microscópica de lâminas contendo amostras de sangue periférico, o que pode ser considerado um trabalho lento e cansativo impactando no desempenho do médico, o desenvolvimento de ferramentas que auxiliem neste processo é uma necessidade real. A proposta deste trabalho é apresentar um algoritmo capaz de segmentar os leucócitos existentes, extrair e selecionar características gerando uma representação compacta e por fim utilizar um classificador para diferenciar imagens de sangue periférico de pacientes saudáveis de pacientes portadores de LLA. A base de imagens ALL_IDB foi escolhida para ser utilizada por ser uma base de domínio público e também utilizada em outros trabalhos permitindo comparações precisas, e apresentar diversas dificuldades encontradas no trabalho com imagens provenientes de microscópio, como diferentes tipos de iluminação e zoom. Das 108 imagens utilizadas nos testes 107 foram classificadas corretamente, resultando em uma acurácia de 0,99 sendo este valor maior que o melhor trabalho encontrado na literatura atual, mesmo o único caso classificado erroneamente foi um falso positivo o que no contexto da aplicação é menos grave do que um falso negativo. Palavras-chave:Leucemia Linfoide Aguda,Processamento Imagens,Reconhecimento?? de??Padrões,Texturas,Classificadores Lineares.Abstract: Acute lymphoblastic leukemia (ALL) is the most common type of cancer in childhood. Despite presenting a rapid evolution in its clinical condition, ALL has a relatively low mortality when identified and treated in its initial stage. Due to the fact that the ALL diagnosis is made by hematologists based on the microscopic analysis of the peripheral blood smear slices, which can be considered a tedious and tiring work, impacting on the doctor's performance, the development of tools that would help in this process is a real necessity. Thus, the purpose of this work is to present an algorithm capable of segmenting the existing leukocytes from blood smear images, extracting and selecting the most representative features, generating a compact representation, so as to finally use a classifier to differentiate the peripheral blood smear images of healthy patients from patients with ALL. The ALL_IDB image base was chosen for being a public domain base and also used in other works, thus allowing accurate comparisons, as well as revealing several difficulties that are faced when working with microscopic images, such as different types of lighting and distinct zoom levels. The final results were expressive and reached an accuracy of 0.99, where, from the 108 images used in the tests, 107 were classified correctly. This result is higher than the best one found in the latest literature, and the only image classified as being wrong was a false positive which in the application context is not the worse case scenario. Keywords: Acute Lymphoid Leukemia, Image??Processing, Pattern??Recognition,Textures, Linear Classifiers

An intelligent decision support system for acute lymphoblastic leukaemia detection

The morphological analysis of blood smear slides by haematologists or haematopathologists is one of the diagnostic procedures available to evaluate the presence of acute leukaemia. This operation is a complex and costly process, and often lacks standardized accuracy owing to a variety of factors, including insufficient expertise and operator fatigue. This research proposes an intelligent decision support system for automatic detection of acute lymphoblastic leukaemia (ALL) using microscopic blood smear images to overcome the above barrier. The work has four main key stages. (1) Firstly, a modified marker-controlled watershed algorithm integrated with the morphological operations is proposed for the segmentation of the membrane of the lymphocyte and lymphoblast cell images. The aim of this stage is to isolate a lymphocyte/lymphoblast cell membrane from touching and overlapping of red blood cells, platelets and artefacts of the microscopic peripheral blood smear sub-images. (2) Secondly, a novel clustering algorithm with stimulating discriminant measure (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of the nucleus and cytoplasm of lymphocytic cell membranes. The SDM measures are used in conjunction with Genetic Algorithm for the clustering of nucleus, cytoplasm, and background regions. (3) Thirdly, a total of eighty features consisting of shape, texture, and colour information from the nucleus and cytoplasm of the identified lymphocyte/lymphoblast images are extracted. (4) Finally, the proposed feature optimisation algorithm, namely a variant of Bare-Bones Particle Swarm Optimisation (BBPSO), is presented to identify the most significant discriminative characteristics of the nucleus and cytoplasm segmented by the SDM-based clustering algorithm. The proposed BBPSO variant algorithm incorporates Cuckoo Search, Dragonfly Algorithm, BBPSO, and local and global random walk operations of uniform combination, and Lévy flights to diversify the search and mitigate the premature convergence problem of the conventional BBPSO. In addition, it also employs subswarm concepts, self-adaptive parameters, and convergence degree monitoring mechanisms to enable fast convergence. The optimal feature subsets identified by the proposed algorithm are subsequently used for ALL detection and classification. The proposed system achieves the highest classification accuracy of 96.04% and significantly outperforms related meta-heuristic search methods and related research for ALL detection