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56 articles for “Pre-Image”
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Research ArticleOpen accessEvery word in the titleImage Pre-Processing Techniques to Improve DBT Classification Accuracy With CNN ResNet-50Qodri Aziz, Slamet Riyadi et al.Digital Breast Tomosynthesis (DBT) is a three-dimensional imaging technology that is meant to determine breast cancer more accurately than regular mammograms. But low contrast and noise can make DBT images less clear, which can make deep learning-based classification less accurate. This study seeks to assess the impact of diverse preprocessing techniques on breast cancer classification outcomes utilizing the ResNet-50 Convolutional … …Journal of Computer Science, Vol. 22, Issue 10 · 21 Oct 2026 · 349 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEfficient Pre-processing of USF and MIAS Mammogram ImagesAyman A. AbuBaker, R. S. Qahwaji, Musbah J. Aqel et al.High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. In this paper, a pre-processing technique for reducing the size and enhancing the quality of USF and MIAS mammogram images is introduced. … …Journal of Computer Science, Vol. 3, Issue 2 · 28 Feb 2007 · 7,167 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative Analysis of Satellite Image Pre-Processing TechniquesT. Sree Sharmila, K. Ramar, T. SreeRenga RajaSatellite images are corrupted by noise in its acquisition and transmission. The removal of noise from the image by attenuating the high frequency image components, removes some important details as well. In order to retain the useful information and improve the visual appearance, an effective denoising and resolution enhancement techniques are required. In this research, Hybrid Directional Lifting (HDL) technique … …Journal of Computer Science, Vol. 9, Issue 2 · 1 Apr 2013 · 7,308 accessesView PDFCite
Research ArticleOpen accessIn the keywordsCAVITY SEGMENTATION AND STROKE VOLUME MEASUREMENT METHODS FROM ECHOCARDIOGRAPHIC IMAGES: REVIEWAnas A. Abboud, Rahmita Wirza Rahmat, Suhaini Bin Kadiman et al.The complexity and criticality of heart disease diagnosis and treatment have frequently attracted the attention of researchers to develop approaches that can handle accurate clinical assessments. One of these complexities is measuring the Right Ventricle stroke volume. Therefore, different cardiac imaging techniques have been developed to help specialists in their regular clinical work and in the operating theatre to assist … …Journal of Computer Science, Vol. 10, Issue 11 · 20 Dec 2014 · 8,410 accessesView PDFCite
Research ArticleOpen accessIn the keywordsHYPERPARAMETER SELECTION IN KERNEL PRINCIPAL COMPONENT ANALYSISMd. Ashad Alam, Kenji FukumizuIn kernel methods, choosing a suitable kernel is indispensable for favorable results. No well-founded methods, however, have been established in general for unsupervised learning. We focus on kernel Principal Component Analysis (kernel PCA), which is a nonlinear extension of principal component analysis and has been used electively for extracting nonlinear features and reducing dimensionality. As a kernel method, kernel PCA … …Journal of Computer Science, Vol. 10, Issue 7 · 15 Feb 2014 · 8,721 accessesView PDFCite
Research ArticleOpen accessIn the abstractLeveraging Modified Cascaded Fully Convolutional Neural Networks and Optimized Swin U-Net for Liver Lesion PredictionSugandha Chakraverti, Tejaswi Khanna, Vijay ShuklaLiver lesions are abnormal growths that can develop for various reasons, some of which are noncancerous (benign) while others are cancerous. Machine learning and, deep learning techniques are employed for liver health assessment and lesion detection, but the quality and, diversity of training data can hinder the performance, leading to overfitting or underfitting. These models often need extensive pre-processing and … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 372 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvancing Digital Forgery Detection: A Comparative Analysis of U-Net, Buster Net, Efficient B0+ U-Net, and VGG 16+ U-Net ModelsPreeti Dhiman, Usha ChauhanForgery Detection in the area of digital images is indispensable for insuring authenticity, particularly in detecting Copy-Move, Splicing. In this research, a meticulous analysis of latest learning architectures i.e. U-Net, Buster Net, Efficient B0+U-Net and VGG16+U-Net are compared for the detecting these types of forgeries. U-Net is popular due to its potent segmentation capabilities to provide pixel-level forgery localization while … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 383 accessesView PDFCite
Research ArticleOpen accessIn the abstractMulti-FOM: A Unified Multimodal Deep Learning Framework for Intelligent Pre-Harvest Papaya Classification and Crop ValorisationSovers Singh Bisht, Sanjeev Thakur, Sanjeev PanwarIntelligent pre-harvest crop assessment has become an essential component of precision agriculture, where accurate crop quality evaluation enables informed decision-making and sustainable crop management. However, existing multimodal frameworks often exhibit limited data diversity and insufficient cross-modal semantic interaction, reducing the reliability of intelligent crop valorisation. Address these limitations, this paper proposes a Multimodal Focal-Mamba Optimized Valorisation Model (Multi-FOM), a unified … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 670 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Approaches for Early Detection of Skin Cancer Disease Detection Using Segmentation and Classification with Severity AnalysisPunam R. Patil, Ritu TandonThe utmost prevalent type of the disease, skin cancer claims millions of lives every year, poses a serious public health threat particularly melanoma, which is often fatal if not detected early. Early diagnosis is crucial, yet traditional methods frequently fall short due to image quality limitations and the complexity of visual differentiation. With an emphasis on severity analysis, this study … …Journal of Computer Science, Vol. 21, Issue 9 · 24 Nov 2025 · 2,051 accessesView PDFCite
Research ArticleOpen accessIn the abstractEmpirical Study on Modified Pre-Trained CNN Architectures for Fitzpatrick17k Skin Diseases Prediction ModellingSteven Matthew, Nur Afny Catur Andryani, Rojali et al.Preventing missed diagnosis of skin diseases is critical for enhancing patient outcomes. Technology support empowered by AI based algorithms have been recently developed to minimize the burden. Convolutional Neural Networks (CNNs) are increasingly utilized for image classification tasks including skin's diseases prediction modelling. Its advancement architectures such as pre-trained models, VGG16 andResNet50 provide a strong foundation to up-skill the prediction … …Journal of Computer Science, Vol. 21, Issue 7 · 8 Jul 2025 · 3,934 accessesView PDFCite