PurposeVarious brain atlases are available to parcellate and analyze brain connections. Most traditional machine learning and deep learning studies analyzing Attention Deficit Hyperactivity Disorder (ADHD) have used either one or two brain atlases for their analysis. However, there is a lack of comprehensive research evaluating the impact of different brain atlases and associated factors such as connectivity measures and dimension reduction techniques on ADHD diagnosis.ApproachThis paper proposes an efficient and robust multimodality model that investigates various brain atlases utilizing different parcellation strategies and scales. Thirty combinations of six brain atlases and five distinct machine learning classifiers with optimized hyperparameters are implemented to identify the most promising brain atlas for ADHD diagnosis. These outcomes are validated using the statistical Friedman test. To enhance comprehensiveness, the impact of three different connectivity measures, each representing unique facets of brain connectivity, is also analyzed. Considering the extensive complexity of brain interconnections, the effect of various dimension reduction techniques on classification performance and execution time is also analyzed. The final model is integrated with phenotypic data to create an efficient multimodal ADHD classification model.ResultsExperimental results on the ADHD-200 dataset demonstrate a significant variation in classification performance introduced by each factor. The proposed model outperforms many state-of-the-art ADHD approaches and achieves an accuracy of 77.59%, an area under the curve (AUC) score of 77.25% and an F1-score of 75.43%.ConclusionsThe proposed model offers clear guidance for researchers, helping to standardize atlas selection and associated factors and improve the consistency and accuracy of ADHD studies for more reliable clinical applications.
KEYWORDS: Sensors, Image segmentation, Detection and tracking algorithms, Single mode fibers, Fuzzy logic, Signal to noise ratio, Data processing, Algorithm development, Image sensors, Edge detection
The inherent memory effect of the Grunwald–Letnikov fractional derivatives is combined with the effectiveness of information sets for detecting edges in digital images. Fractional derivatives are utilized in the computation of fractional gradients, which are further processed using information sets for the proposed edge detector to extract more edges than possible with the traditional edge detectors. In the proposed approach, first the gradient operators of the Sobel mask are converted into the fractional form and convolved with the given gray image. In the next step, the histogram of the fractional gradients is fuzzified using the Gaussian membership function and the sigmoidal membership function. The optimal parameters for these membership functions are selected through a grid search method based on the information set-based edge strength factor ESf. In the final step, defuzzification is performed to obtain the final edge maps. By plotting the edge maps, analyzing the boundary information, and Pratt’s figure of merit scores for images in the Berkeley segmentation dataset, it is observed that the resulting edge maps of the proposed edge detector contain more quantitative and qualitative information than that of traditional edge detectors even in the presence of noise.
KEYWORDS: Edge detection, Sensors, Image segmentation, Detection and tracking algorithms, Fuzzy logic, Image processing, Digital imaging, Single mode fibers, Digital color imaging, Image quality
Most image processing and computer vision applications require edge detection for object recognition, image segmentation, and scene analysis. The traditional algorithms cannot handle the demanding requirements on the accuracy and robustness of these applications. Information set theory is utilized in this paper for defining edge strength measures which help in finding robust edges. The proposed work is originated from the smallest univalue segment assimilating nucleus concept, wherein a mask is applied on the red, green, and blue components of the color image for calculating a small area of neighboring pixels with similar brightness to center pixels. A symmetric Gaussian membership function (MF) is used to fuzzify the histogram of this area. This MF is converted into sigmoidal MF to strengthen and sharpen the weak edges. These two MFs provide the best results in comparison to other MFs used in literature. Extensive simulation results show that the proposed technique produces better results than other existing techniques in terms of the qualitative and quantitative measures, which include Pratt’s figure of merit, structural similarity index, and analysis of variance. The proposed technique also works well in the presence of impulse noise.
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