Navegando por Autor "Diniz, Débora Nasser"

Agora exibindo 1 - 5 de 5

An ensemble method for nuclei detection of overlapping cervical cells.
(2021) Diniz, Débora Nasser; Vitor, Rafael Ferreira; Bianchi, Andrea Gomes Campos; Silva, Saul Emanuel Delabrida; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi; Medeiros, Fátima Nelsizeuma Sombra de; Souza, Marcone Jamilson Freitas
The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988).
Contributions to automating the analysis of conventional Pap smears.
(2023) Diniz, Débora Nasser; Souza, Marcone Jamilson Freitas; Bianchi, Andrea Gomes Campos; Souza, Marcone Jamilson Freitas; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Luz, Eduardo José da Silva; Pessin, Gustavo; Souza, Jefferson Rodrigo de; Veras, Rodrigo de Melo Souza
This thesis, organized as a compilation of articles, develops and presents contri- butions to the automated analysis of conventional Pap smear slides. A conventional Pap smear slide is a sample of cervical cells collected and prepared on a glass slide for subsequent cytopathological analysis. The main contributions are to detect and classify cervical cell nuclei to develop a decision support tool for cytopathologists. The first arti- cle resulting from this research utilizes a hierarchical methodology using Random Forest for the nucleus classification of the Herlev and Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database databases based on 232 handcrafted fea- tures. In this article, we investigate balancing techniques, perform statistical analyses using Shapiro-Wilk and Kruskal-Wallis tests, and introduce the CRIC Searchable Image Database segmentation base. Our result defined the state-of-the-art in five metrics for nucleus classification in five and seven classes and the state-of-the-art in precision and F1-score for two-class classification. The second article introduces a method for nu- cleus detection in synthetic Pap smear images from the Overlapping Cervical Cytology Image Segmentation Challenge dataset proposed at the 11th International Symposium on Biomedical Imaging (ISBI’14). In this second article, we investigate clustering al- gorithms for image segmentation. We also explore four traditional machine learning techniques (Decision Tree (DT), Nearest Centroid (NC), k-Nearest Neighbors (k-NN), and Multi-layer Perceptron (MLP)) for classification and propose an ensemble method using DT, NC, and k-NN. Our result defined the state-of-the-art recall using this dataset. The third article proposes an ensemble method using EfficientNets B1, B2, and B6 to classify images from the CRIC Searchable Image Database dataset. Here, we investigate ten neural network architectures to choose those used in the ensemble method and present a data augmentation methodology using image transformation techniques. Our result de- fined the five state-of-the-art metrics for nucleus classification in two and three classes. Furthermore, we introduce results for six-class classification. Lastly, the fourth article introduces the Cytopathologist Eye Assistant (CEA), an intuitive and user-friendly tool that uses deep learning to detect and classify cervical cells in Pap smear images, support- ing cytopathologists in providing diagnoses. We investigate You Only Look Once (YOLO) v5 and YOLOR for performing both tasks (detection and classification) and also explore the combination of using YOLOv5 for detection and the ensemble of EfficientNets from the third article for classification. The article explores data balancing techniques, under- sampling, and oversampling using Python’s Clodsa library. The CRIC Cervix database was used for tool evaluation, considering four scenarios: original images, resized im- ages, augmented resized images, and balanced resized images. The application of CEA was validated by specialists with years of experience in cytopathology, highlighting the tool’s ease of use and potential to address specific queries.
A cytopathologist eye assistant for cell screening.
(2022) Diniz, Débora Nasser; Keller, Breno Nunes de Sena; Rezende, Mariana Trevisan; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Oliveira, Renata Rocha e Rezende; Luz, Eduardo José da Silva; Ushizima, Daniela Mayumi; Medeiros, Fátima Nelsizeuma Sombra de; Souza, Marcone Jamilson Freitas
Screening of Pap smear images continues to depend upon cytopathologists’ manual scrutiny, and the results are highly influenced by professional experience, leading to varying degrees of cell classification inaccuracies. In order to improve the quality of the Pap smear results, several efforts have been made to create software to automate and standardize the processing of medical images. In this work, we developed the CEA (Cytopathologist Eye Assistant), an easy-to-use tool to aid cytopathologists in performing their daily activities. In addition, the tool was tested by a group of cytopathologists, whose feedback indicates that CEA could be a valuable tool to be integrated into Pap smear image analysis routines. For the construction of the tool, we evaluate different YOLO configurations and classification approaches. The best combination of algorithms uses YOLOv5s as a detection algorithm and an ensemble of EfficientNets as a classification algorithm. This configuration achieved 0.726 precision, 0.906 recall, and 0.805 F1-score when considering individual cells. We also made an analysis to classify the image as a whole, in which case, the best configuration was the YOLOv5s to perform the detection and classification tasks, and it achieved 0.975 precision, 0.992 recall, 0.970 accuracy, and 0.983 F1-score.
A hierarchical feature-based methodology to perform cervical cancer classification.
(2021) Diniz, Débora Nasser; Rezende, Mariana Trevisan; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi; Medeiros, Fátima Neusizeuma Sombra de; Souza, Marcone Jamilson Freitas
Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.
Segmentação de núcleos em células cervicais obtidas em exames de Papanicolaou.
(2019) Diniz, Débora Nasser; Souza, Marcone Jamilson Freitas; Bianchi, Andrea Gomes Campos; Souza, Marcone Jamilson Freitas; Bianchi, Andrea Gomes Campos; Medeiros, Fátima Nelsizeuma Sombra de; Penna, Puca Huachi Vaz
Este trabalho tem seu foco na detecção de núcleos em imagens sintéticas de células cervicais. Este é um passo importante na construção de uma ferramenta computacional para ajudar os citopatologistas a identificarem alterações celulares a partir de exames de Papanicolaou. Para detectar esses núcleos propomos duas abordagens, a primeira baseada em Iterated Local Search (ILS) e a segunda em Árvore de Decisão (DT). O objetivo é melhorar a assertividade do exame e reduzir a carga de trabalho do profissional. As duas abordagens utilizam características de uma região da imagem para identificar um núcleo. Para ambas, foi necessário fazer um pré-processamento das imagens para dividí-las em regiões a serem analisadas. Para isto, foram utilizados os algoritmos Simple Linear Iterative Clustering (SLIC) e Density Based Spatial Clustering of Applications with Noise (DBSCAN). No ILS, foi feita uma investigação para saber quais dessas características são relevantes para a identificação dos núcleos. O pacote irace foi utilizado para fazer a calibração automática dos parâmetros do ILS. Já para a DT proposta, foi construída uma base de dados com todas as características extraídas das regiões e feita uma seleção das mais importantes por meio de uma matriz de correlação. Com essas características selecionadas foi feito o treinamento. Por fim, as abordagens propostas foram comparadas entre si e com outros métodos da literatura segundo as métricas revocação, precisão e F1, usando-se o banco de dados ISBI Overlapping Cytology Image Segmentation Challenge (2014). Os resultados obtidos mostraram a superioridade da abordagem via DT sobre o ILS em todas as métricas, assim como sua superioridade sobre todos os outros métodos da literatura com relação às métricas F1 e revocação.