Comparative Analysis of CLAHE and AHE on Application of CNN Algorithm in the Detection of Covid-19 Patients

Umri, Buyut Khoirul; Utami, Ema; Kurniawan, Mei P

doi:10.1109/icoiact53268.2021.9563980

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…According to the findings of this study, GC has the highest sensitivity, whereas CLAHE has the highest accuracy in detecting GC. According to [40] research, the CLAHE and adaptive histogram equalization (AHE) methods are used to identify COVID-19 using the VGG19 model, with the goal of identifying COVID-19 [41] using the VGG19 model. Table 1 presents a comparative analysis of recent research findings.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Enhance‐Net: An Approach to Boost the Performance of Deep Learning Model Based on Real‐Time Medical Images

et al. 2023

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Real-time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance-Net. It is possible to classify the experiment into six distinct stages. Champion-Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet-18, and VGG-19). This stage helps choose the optimal model. In the second step, Champion-Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion-Net performance. The next stage extracts green channel data. In the fourth step, Champion-Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance-performance. The next stage compares the Enhance-Net findings to the lightness order error (LoE). In Enhance-Net models, the current study combines image enhancement and green channel with Champion-Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real-time medical image prediction. The study effort uses the musculoskeletal radiograph-bone classification (MURA-BC) dataset. Classification accuracy of Enhance-Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real-time testing with the unseen dataset.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Enhance‐Net: An Approach to Boost the Performance of Deep Learning Model Based on Real‐Time Medical Images

et al. 2023

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“…Dimana hasil data menunjukkan bahwa pada tahun 2018 merupakan tahun terburuk karena tercatat sebanyak 381 hari di level tidak sehat dan sebanyak 30 hari di level sangat tidak sehat jika dilihat dari total pengukuran di lima kota besar DKI Jakarta. Sedangkan, terdapat penelitian yang dilaksanakan oleh Umri et al (2021) di DKI Jakarta yang menyatakan bahwa antara tahun 2017 dan 2019, hanya di Jakarta yang mengalami kualitas udara semakin memburuk dari hari ke hari. Ini didasarkan pada pemantauan kualitas udara yang dilakukan 104 Analisis Perbandingan Pencemaran Udara Selama dan Setelah Pandemi Covid-19 di DKI Jakarta (Fatimah, dkk) oleh US Air Quality Index (AQI) di tahun 2019.…”

Section: Analisis Kondisi Pencemaran Udara DI Dki Jakartaunclassified

Analisis Perbandingan Pencemaran Udara Selama dan Setelah Pandemi Covid-19 di DKI Jakarta

Fatimah,

Sudarti,

Yushardi

2024

saintifik

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Pandemi Covid-19 memberikan dampak positif pada lingkungan sekitar, terutama pada udara. DKI Jakarta termasuk salah satu kota yang terdampak adanya pandemi ini dan juga mempunyai tingkat polusi udara yang sangat tinggi. Provinsi DKI Jakarta merupakan Ibu Kota Negara Indonesia yang memiliki penduduk yang sangat banyak. DKI Jakarta juga berkontribusi pada gas emisi udara yang dapat menyebabkan penurunan kualitas udara melalui aktivitas pemukiman, industri, dan transportasi. Indeks kualitas udara di DKI Jakarta didominasi oleh kondisi yang bahkan berada pada level berbahaya. Dengan adanya sistem pandemi ini dapat menyebabkan Pemerintah Provinsi DKI Jakarta memutuskan kebijakan lockdown pada bulan Maret 2020. Menurut studi di beberapa negara, terjadi peningkatan kualitas udara akibat terhentinya mobilitas masyarakat. Untuk itu penelitian ini bertujuan untuk mengetahui bagaimana perbandingan kualitas udara sebelum dan pada saat pandemi Covid-19 di DKI Jakarta serta solusi apa yang tepat dalam menangani hal tersebut. Penelitian ini menggunakan metode studi literatur dengan mengumpulkan beberapa jurnal dan buku. Hasil analisis menunjukkan bahwa kondisi udara di DKI Jakarta selama diberlakukannya sistem lockdown memiliki kualitas udara cukup sehat dibandingkan dengan tahun setelah terjadinya pandemi serta dengan adanya penambahan Ruang Terbuka Hijau (RTH) akan menjadi solusi yang tepat dalam menangani permasalahan diatas.

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“…Since CLAHE enhances the contrast by correlating the intensities of neighboring pixels, less noise is seen in the output image compared to AHE. In this way, brightness bursts in the image are prevented [15][16][17]. After illumination and contrast enhancement, the image is converted from the HSI color space back to the RGB color space by the equations given below:…”

Section: Preprocessing and Roimentioning

confidence: 99%

Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots

ILGIN,

AYDEMİR,

CEDİMOĞLU

et al. 2023

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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Accurate detection of tomatoes grown in greenhouses is important for timely harvesting. In this way, it is ensured that mature tomatoes are collected by distinguishing them from the unripe ones. Insufficient light, occlusion, and overlapping adversely affect the detection of mature tomatoes. In addition, it is time consuming for people to detect mature tomatoes at certain periods in large greenhouses. For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today. In this paper, two feature extraction methods, histogram of oriented gradients (HOG) and local binary patterns (LBP), which are effective in object recognition, and two important and commonly used classifiers of machine learning, support vector machines (SVM) and k-nearest neighbor (kNN), are comparatively used to detect and count tomatoes. The HOG and LBP features are classified separately and together by SVM or kNN, and the success of each case are compared. Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.

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Comparative Analysis of CLAHE and AHE on Application of CNN Algorithm in the Detection of Covid-19 Patients

Cited by 5 publications

References 15 publications

[Retracted] Enhance‐Net: An Approach to Boost the Performance of Deep Learning Model Based on Real‐Time Medical Images

[Retracted] Enhance‐Net: An Approach to Boost the Performance of Deep Learning Model Based on Real‐Time Medical Images

Analisis Perbandingan Pencemaran Udara Selama dan Setelah Pandemi Covid-19 di DKI Jakarta

Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots

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