Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images

Ngân, Trần Thị; Tuấn, Trần Mạnh; Sơn, Lê Hoàng; Minh, Nguyen Hai; Dey, Nilanjan

doi:10.1007/s10916-016-0634-y

Cited by 56 publications

(26 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modified Hopfield neural network [26] or advanced fuzzy decision making [28] would be the solution to our problem, which warrants further research. Moreover, similar to the research in [29,30], the proposed algorithms need to be applied to different datasets.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

et al. 2020

Self Cite

View full text Add to dashboard Cite

In dental diagnosis, recognizing tooth complications quickly from radiology (e.g., X-rays) takes highly experienced medical professionals. By using object detection models and algorithms, this work is much easier and needs less experienced medical practitioners to clear their doubts while diagnosing a medical case. In this paper, we propose a dental defect recognition model by the integration of Adaptive Convolution Neural Network and Bag of Visual Word (BoVW). In this model, BoVW is used to save the features extracted from images. After that, a designed Convolutional Neural Network (CNN) model is used to make quality prediction. To evaluate the proposed model, we collected a dataset of radiography images of 447 patients in Hanoi Medical Hospital, Vietnam, with third molar complications. The results of the model suggest accuracy of 84% ± 4%. This accuracy is comparable to that of experienced dentists and radiologists.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In future works, we will make the implementations on a wider range of different, complex dental image datasets to verify the suitability of this algorithm. Other integrations between deep learning and fuzzy computing in our previous studies [28,31,32] would be vital for favorable outcomes in upcoming research.…”

Section: Discussionmentioning

confidence: 99%

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using the existing system (i.e., sparse representation), the accuracy of the HR image was 80%, but using the proposed method (global face sparse representation), the accuracy of the HR image was 85-90%. In the future, we will study various methods [32][33][34][35][36][37][38][39][40][41][42][43] and enhance the algorithm on posed face images. The computational complexity of the proposed system was analyzed using the big oh notation O n 2 K M , for the face hallucination methodology.…”

Section: Discussionmentioning

confidence: 99%

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

et al. 2020

Self Cite

View full text Add to dashboard Cite

This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

show abstract

“…where R a and R b are random numbers having the range [0,1] and X r,j is the knowledge (position) value of the chosen individual. From Equations (2) and 3, it can be observed that the implementation of the SGO algorithm is simple compared to other algorithms existing in the image processing domain [40][41][42][43][44][45][46][47][48]. The steps of the standard SGO algorithm can be described as in Algorithm 1.…”

Section: Social Group Optimizationmentioning

confidence: 99%

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

et al. 2018

Self Cite

View full text Add to dashboard Cite

The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard's coefficient, Dice's coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur's thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy.

show abstract

Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images

Cited by 56 publications

References 14 publications

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Contact Info

Product

Resources

About