Detail Study of Different Algorithms for Early Detection of Cancer

Dhar, Prasenjit; Devi, K. Suganya; Satti, Satish Kumar; Srinivasan, Padmini

doi:10.1007/978-981-15-9735-0_12

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…The KNN algorithm is an unsupervised learning technique 26 . KNN uses similarity measurements to classify new data.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…KNN uses similarity measurements to classify new data. The procedure was completed with the help of a majority of the neighbors 26 . The minimum classification error rate for the datasets Chula‐PIC‐Lab (Chula‐RBC‐12) and Erythrocytes IDB is shown in Figures 19 and 20.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The KNN algorithm is an unsupervised learning technique. 26 KNN uses similarity measurements to classify new data. The procedure was completed with the help of a majority of the neighbors.…”

Section: K Nearest Neighbor (Knn)mentioning

confidence: 99%

“…The procedure was completed with the help of a majority of the neighbors. 26 The minimum classification error rate for the datasets Chula-PIC-Lab (Chula-RBC-12) and Erythrocytes IDB is shown in Figures 19 and 20. The proposed hyper-parameter optimizable KNN (HPKNN) classifier used a grid search approach.…”

Section: K Nearest Neighbor (Knn)mentioning

confidence: 99%

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HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

Dhar

Kothandapani

Satti

et al. 2023

Int J Imaging Syst Tech

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Red blood cells (RBCs) are the more crucial components of a healthy organism.Poikilocytosis is a disorder in which blood cells lose their standard shapes. The human bodies are affected by disorders like anemia and thalassemia because the structure of RBCs shape changes. Hematologists take longer time to detect abnormalities in RBCs. The proposed hyper-parameter optimizable K nearest neighbor (HPKNN) algorithm detects and classifies numerous types of poikilocytosis quickly and accurately. On the Chula-PIC-Lab and Erythrocytes IDB datasets, the proposed HPKNN algorithm was tested. After the preprocessing stage, the Freeman chain code has been utilized to retrieve the exact boundary shape of RBC morphology. On datasets of Erythrocytes IDB 1, 2, 3, and Chula-PIC-Lab(Chula-RBC-12), the morphological tools and Freeman chain code based segmentation provide 97.44% and 94.45% accuracy, respectively, which is superior than other methods. The proposed HPKNN classifier has an overall sensitivity of 97.9%, precision of 97.9%, and an f 1 score of 97.9% on the Chula-PIC-Lab (Chula-RBC-12) dataset to classify 10 classes, namely acanthocyte, dacrocyte, echinocyte, elliptocyte, normal, overlapped, schistocyte, spherocyte, stomatocyte, and target cell, which is greater than any other current approach. The HPKNN classifier achieves 98.085% precision, 98.085% sensitivity or recall, and 98.085% f 1 -score values on the Erythrocytes IDB datasets to classify circular, elongated, and other RBC classes.anemia, Freeman chain code, KNN classifier, poikilocytosis | INTRODUCTIONPoikilocytosis means the RBC blood cells are abnormal due to the changes in their standard shape. 1 The normal RBC blood cells look disk-shaped, biconcave, and trim. The RBC blood smear cells have no nucleus, lysosome, mitochondria, and endoplasmic. The diameter of normal RBC cells is 6.2 to 8.2 μ. 2 Generally, it is observed that the diameter of normal RBC cells is 7.2 μ. 2 The RBC cells are responsible for carrying different nutrients and oxygen to our body's organs and tissues. When RBCs' shape varies from normal, they are not having enough nutrients and oxygen. We are suffering from lots of diseases because of a lack of oxygen and nutrients. Poikilocytosis affects the body due to trauma or abnormalities occurring in the membrane. The poikilocytosis abnormality that occurs due to trauma is blister and keratocytes, teardrop cells, schistocytes, pyro-poikilocytes, and semilunar bodies. Poikilocytosis due to abnormalities in the membrane are target cells, burr cells, mouth

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“…The KNN algorithm is an unsupervised learning technique 26 . KNN uses similarity measurements to classify new data.…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Section: K Nearest Neighbor (Knn)mentioning

confidence: 99%

Section: K Nearest Neighbor (Knn)mentioning

confidence: 99%

See 2 more Smart Citations