Brain and Pancreatic Tumor Classification Based on GLCM—k-NN Approaches

Reddy, Divya; Prasath, T. Arun; Murugan, Pallikonda Rajasekaran; Vishnuvarthanan, G.

doi:10.1007/978-981-13-2182-5_28

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…The min-max color space threshold algorithm's accuracy is validated using the canny edge algorithm, and k-means algorithm. Accurate segmentation of medical images leads to the exact location of the tumour [16]. After making validation, results found that the proposed technique produces much better accuracy than other algorithms.…”

Section: Segmentationmentioning

confidence: 95%

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Roy¹,

Mala²

2022

Computer Systems Science and Engineering

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Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean Value, and Mean Standard Deviation are computed after color space conversion from RGB to HSV. Fuzzy k-Nearest Neighbor (F-kNN) is designed for classification. The system proposed is trained and tested using features extracted from jaundiced eye images. The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible, helping clinicians make better decisions for surgical planning.

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Section: Segmentationmentioning

confidence: 95%

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Roy¹,

Mala²

2022

Computer Systems Science and Engineering

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“…The k-NN concept was employed by Kilicet al [ 29 ] to identify colonic polyps using region covariance in CT-colonography images as the distinguishing features. In another report employing k-NN [ 30 ], the gray level co-occurrence matrix was employed as the classifying feature in medical images of the brain and pancreatic cancers. However, k-NN is limited by issues pertaining to local structure sensitivity and the possibility of over-fitting, leading to errors.…”

Section: Ai Models For the Diagnosis Of Pancreatic Cancermentioning

confidence: 99%

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Hameed

Krishnan

2022

Cancers

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Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians’ treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

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“…Supervised methods involve training a classifier with a dataset (training set) to differentiate between vessel and non-vessel pixels, further classified into machine learning and deep learning algorithms. Machine learning approaches typically involve feature extraction, selection, and classification stages, with various feature extractors and classifiers PSO-HRVSO for medical image classification, including bag-of-visual-words, Gaussian filter, and Gabor filter, along with classifiers like K-Nearest Neighbors (K-NN), Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

SIPIJ

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The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature.The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage.The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively.The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

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Brain and Pancreatic Tumor Classification Based on GLCM—k-NN Approaches

Cited by 9 publications

References 16 publications

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

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