Bread and durum wheat classification using wavelet based image fusion

Sabancı, Kadir; Aslan, Muhammet Fatih; Durdu, Akif

doi:10.1002/jsfa.10610

Cited by 20 publications

(8 citation statements)

References 34 publications

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“…WT performs a calculation with variable window sizes to provide more precise time–frequency resolution. In this way, the analysis of non-stationary signals, whose frequency changes over time, is performed in a powerful way [ 61 ]. A mother wavelet is determined for WT, and then the wavelet function is scaled and translated.…”

Section: Methodsmentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

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Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.

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Section: Methodsmentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

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“…Multi-source remote sensing image fusion is an algorithm designed based on practical problem requirements and applications. With improvements in computer performance, hardware technology, and wavelet transforms in recent years, image fusion by wavelet transformation has become a popular research topic [13]. Ranchin et al (1993) were the first to apply wavelet transform to the field of remote sensing image fusion [14].…”

Section: Introductionmentioning

confidence: 99%

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Lu,

Zhou,

Huang

et al. 2024

Sensors

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Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and “NPP-VIIRS-like” night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

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“…Some of the studies on computer vision and artificial intelligence applications developed for agricultural products are available. For example, Sabanci et al [17] used texture properties to classify wheat as bread and durum, thereby automatically determining the quality difference. Wheat images were obtained with two imaging techniques, ultraviolet (UV) and white light (WL).…”

Section: Introductionmentioning

confidence: 99%

Preservation effects evaluated using innovative models developed by machine learning on cucumber flesh

Ropelewska

Sabancı

Aslan

2022

Eur Food Res Technol

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The objective of this study was to compare the slice image features of cucumbers preserved in different ways and fresh cucumbers. The models for discrimination of spontaneous lacto-fermented, pickled using a vinegar solution and fresh cucumber slices were built based on selected textures extracted from digital images converted to color channels L, a, b, R, G, B, U, V, X, Y, Z. The average accuracies were high of up to 98% for the Random Forest and Logistic classifiers for a model built based on textures selected from a set including textures from all color channels. The discrimination accuracies for individual samples were equal to 100% for fresh cucumber (Bayes Net, Logistic and IBk classifiers), 98% for vinegar-pickled cucumber (Random Forest, Logistic), 97% for lacto-fermented cucumber (Random Forest). The values of TP Rate, Precision, PRC Area, ROC Area and F-Measure were the highest for fresh cucumber and were equal to 1.000.

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Bread and durum wheat classification using wavelet based image fusion

Cited by 20 publications

References 34 publications

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Preservation effects evaluated using innovative models developed by machine learning on cucumber flesh

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