Brain tumor detection and classification using SIFT in MRI images

Altaei, Mohammed Sahib Mahdi; Kamil, Sura Yarub

doi:10.1063/5.0031014

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…This approach was also successful in extracting the new texture features for defining the image properties which in turn provided better classification results for diagnosing the cancer. Altaei and Kamil (2020) proposed a method to accurately classify the brain tumor using the SIFT descriptors. Here, a two-level classification model was used.…”

Section: Wearable Iot Based Diagnosismentioning

confidence: 99%

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Wearable IoT based diagnosis of prostate cancer using GLCM-multiclass SVM and SIFT-multiclass SVM feature extraction strategies

Chandrasekhara

Kabadi

Srivinay

2021

IJPCC

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Purpose This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life. Design/methodology/approach The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer. Findings The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set. Originality/value The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

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Section: Wearable Iot Based Diagnosismentioning

confidence: 99%

“…Altaei and Kamil (2020) proposed a method to accurately classify the brain tumor using the SIFT descriptors. Here, a two-level classification model was used.…”

Section: Literature Surveymentioning

confidence: 99%