Oral cancer detection model in distributed cloud environment via optimized ensemble technique

Shetty, Savita; Patil, Annapurna P.

doi:10.1016/j.bspc.2022.104311

Cited by 7 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same research uses SVM for classification, such as research conducted by Wu et al [15] on early identification of gray mold disease on strawberry leaves. Subsequent research used SVM classification as carried out by Shetty and Patil [23]who studied the oral cancer detection framework. The model is used to detect the data pre-processing stage, which is used to reduce noise and remove unwanted artifacts.…”

mentioning

confidence: 99%

The object detection model uses combined extraction with KNN and RF classification

Kurniati,

Manongga,

Sembiring

et al. 2024

IJEECS

View full text Add to dashboard Cite

Object detection plays an important role in various fields. Developing detection models for 2D objects that experience rotation and texture variations is a challenge. In this research, the initial stage of the proposed model integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) texture feature extraction to obtain feature vectors. The next stage is classifying features using k-nearest neighbors (KNN) and random forest (RF), as well as voting ensemble (VE). System testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower. Although GLCM features improve performance on both algorithms, KNN is more consistent. The VE approach provides the best performance with an accuracy of 93.9% and an F1-score of 93.8%, this shows the effectiveness of the ensemble technique in increasing object detection accuracy. This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification.

show abstract

mentioning

confidence: 99%