Content-Based Quality Estimation for Automatic Subject Indexing of Short Texts Under Precision and Recall Constraints

Toepfer, Martin; Seifert, Christin

doi:10.1007/978-3-030-00066-0_1

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…TELKOMNIKA Telecommun Comput El Control  Classification of melanoma skin cancer using deep learning approach (Maha Ali Hussein) 135 b) Recall: the degree of precision indicates the percentage of data points this model correctly identified as relevant. The capacity to locate every pertinent example in datasets is known as precision [26]. In (9) shows that.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Classification of melanoma skin cancer using deep learning approach

Hussien,

Hassin Alasadi

2024

TELKOMNIKA

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In this study, the authors propose a deep learning (DL) approach for classifying melanoma skin cancer (MSC). They introduce a convolution neural network (CNN) model that consists of 27 layers, which are carefully designed to extract features from skin lesion images and classify them into melanoma and non-melanoma classes. The proposed CNN model comprises multiple convolution layers that apply filters to the input image to extract features such as edges, shapes, and patterns. Batch normalization layers that normalize the output of the convolution layers to accelerate the learning process and prevent overfitting follow these convolution layers. The performance of the proposed CNN model was evaluated on publicly available datasets of skin lesion images, and the findings showed that it outperformed several state-of-the-art methods for melanoma classification. The authors also conducted ablation studies to analyze each layer's contribution to the model's overall performance. The proposed DL approach has the potential to assist dermatologists in the early detection of MSC, which can lead to treatment that is more effective and improves patient outcomes. It also demonstrates the effectiveness of DL techniques for medical image analysis and highlights the importance of carefully designing and optimizing CNN models for high performance. The accuracy of the proposed system is 99.99%.

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Section: Evaluation Metricsmentioning

confidence: 99%

Classification of melanoma skin cancer using deep learning approach

Hussien,

Hassin Alasadi

2024

TELKOMNIKA

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“…Where TP: True Positive and FP: False Positive cases B. Recall: The ability to locate all of the relevant examples in a dataset is the ratio of the data points that were relevant in this model states [33].…”

Section: Performance Measuresmentioning

confidence: 99%

Diagnose Eyes Diseases Using Various Features Extraction Approaches and Machine Learning Algorithms

Najm Abed,

M Al-Bakry

2023

ijci

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Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm).

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