scholarly journals Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 15
Author(s):  
Subrata Bhattacharjee ◽  
Kobiljon Ikromjanov ◽  
Kouayep Sonia Carole ◽  
Nuwan Madusanka ◽  
Nam-Hoon Cho ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Cluster Analysis ◽  
Performance Metrics ◽  
Minimum Spanning Tree ◽  
Cell Nuclei ◽  
Model Quality ◽  
Statistical Parameters ◽  
Nuclei Segmentation ◽  
Grade Groups

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

scholarly journals Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Khin Yadanar Win ◽  
Somsak Choomchuay ◽  
Kazuhiko Hamamoto ◽  
Manasanan Raveesunthornkiat
Keyword(s):  
Pleural Effusion ◽  
Comparative Study ◽  
Performance Metrics ◽  
Mean Shift ◽  
Automated Analysis ◽  
Cell Nuclei ◽  
Detection Rates ◽  
Nuclei Segmentation ◽  
Segmentation Methods ◽  
Aided Diagnosis

Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. Studies on the automated analysis of cytology pleural effusion images are few because of the lack of reliable cell nuclei segmentation methods. Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images. Each method involves three main steps: preprocessing, segmentation, and postprocessing. The preprocessing and segmentation stages help enhancing the image quality and extracting the nuclei regions from the rest of the image, respectively. The postprocessing stage helps in refining the segmented nuclei and removing false findings. The segmentation methods are quantitatively evaluated for 35 cytology images of pleural effusion by computing five performance metrics. The evaluation results show that the segmentation performances of the Otsu, k-means, mean shift, Chan–Vese, and graph cut methods are 94, 94, 95, 94, and 93%, respectively, with high abnormal nuclei detection rates. The average computational times per image are 1.08, 36.62, 50.18, 330, and 44.03 seconds, respectively. The findings of this study will be useful for current and potential future studies on cytology images of pleural effusion.

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

Automated Cell Nuclei Segmentation on Cervical Smear Images Using Structure Analysis

2021 ◽  
Vol 51 ◽  
pp. 105-115
Author(s):  
Wan Azani Mustafa ◽  
Low Zhe Wei ◽  
Khairul Shakir Ab Rahman
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Standard Technique ◽  
Successful Implementation ◽  
Cell Nuclei ◽  
Structural Element ◽  
Detection Techniques ◽  
Nuclei Segmentation ◽  
Common Cancer ◽  
Early Century

Cervical cancer is a common cancer that affects women around the world, and it is also the most common cancer in the developing countries. The cancer burden has increased due to several factors, such as population growth and ageing. In the early century, the systematization of cervical cancer cells takes some time to process manually, and the result that comes out is also inaccurate. This article presents a new nucleus segmentation on pap smear cell images based on structured analysis or morphological approach. Morphology is a broad set of image processing operations that process images based on shape, size and structure. This operation applies a structural element of the image to create an output image of the same size. The most basic of these operations are dilation and erosion. The results of the numerical analysis indicate that the proposed method achieved about 94.38% (sensitivity), 82.56% (specificity) and 93% (accuracy). Also, the resulting performance was compared to a few existing techniques such as Bradley Method, Nick Method and Sauvola Method. The results presented here may facilitate improvements in the detection method of the pap smear cell image to resolve the time-consuming issue and support better system performance to prevent low precision result of the Human Papilloma Virus (HPV) stages. The main impact of this paper is will help the doctor to identify the patient disease based on Pap smear analysis such as cervical cancer and increase the percentages of accuracy compared to the conventional method. Successful implementation of the nucleus detection techniques on Pap smear image can become a standard technique for the diagnosis of various microbiological infections such as Malaria and Tuberculosis.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

Urology journals recommend new prostate cancer grade groups

2016 ◽  
Vol 13 (7) ◽  
pp. 374-375 ◽  
Author(s):  
Jonathan I. Epstein
Keyword(s):  
Prostate Cancer ◽  
Grade Groups

scholarly journals HEIGHT-DIAMETER MODELS FOR THREE SUBTROPICAL FOREST TYPES IN SOUTHERN BRAZIL

2015 ◽  
Vol 39 (3) ◽  
pp. 205-215 ◽  
Author(s):  
Alexander Christian Vibrans ◽  
Paolo Moser ◽  
Laio Zimermann Oliveira ◽  
João Paulo de Maçaneiro
Keyword(s):  
Deciduous Forest ◽  
Tree Height ◽  
Information Criterion ◽  
Confidence Bands ◽  
Natural Forests ◽  
Forest Types ◽  
Model Quality ◽  
Statistical Parameters ◽  
Santa Catarina ◽  
Ombrophilous Forest

Total tree height (h) is often difficult to measure in natural forests. Regression models based on easily accessed variables like DBH (d) can be an alternative, since their assumptions are validated. The aims of this study are to: (i) calibrate specific and generic h-d models for three forest types (Seasonal Deciduous Forest, DEC; Mixed Ombrophilous Forest, MIX; and Dense Rainforest, DEN) in Santa Catarina state testing the regression assumptions and evaluating model quality; (ii) verify different h-d relationship between forest types. The dataset (1,766 measured tree h and 3,150 estimated h) was collected by Santa Catarina Forest and Floristic Inventory (IFFSC) in 418 systematically located sample plots. Models were calibrated for two datasets, one containing hypsometer measurements, the other h estimations made by field crews. Specific models were calibrated for species with at least 30 sampled trees. Residual normality, randomness and heteroskedasticity were evaluated by analytical methods. Confidence bands were generated by the Working-Hotelling method; z test for means was applied to compare models based on the two databases. The statistical parameters such as corrected Akaike Information Criterion provided evidences that logarithmic models were better adjusted to the data. Both datasets were statistically different for DEN and MIX. Differences in h-d relationships were found between forest types. The use of calibrated h-d models is an alternative for studying the relationships between these variables and to assess vertical structure patterns of forest communities, when h measurements are not feasible, although, for situations that more accurate h values are needed, they will not always provide reliable predictions.

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