Optimized Spatial Transformer for Segmenting Pancreas Abnormalities

Abstract

The precise delineation of the pancreas from clinical images poses a substantial obstacle in the realm of medical image analysis and surgical procedures. Challenges arise from the complexities of clinical image analysis and complications in clinical practice related to the pancreas. To tackle these challenges, a novel approach called the Spatial Horned Lizard Attention Approach (SHLAM) has been developed. As a result, a preprocessing function has been developed to examine and eliminate noise barriers from the trained MRI data. Furthermore, an assessment of the current attributes is conducted, followed by the identification of essential elements for forecasting the impacted region. Once the affected region has been identified, the images undergo segmentation. Furthermore, it is crucial to emphasize that the present study assigns 80% of the data for training and 20% for testing purposes. The optimal parameters were assessed based on precision, accuracy, recall, F-measure, error rate, Dice, and Jaccard. The performance improvement has been demonstrated by validating the method on various existing models. The SHLAM method proposed demonstrated an accuracy rate of 99.6%, surpassing that of all alternative methods.

Keywords: Feature extraction; Magnetic resonance imaging; Pancreas; Preprocessing; Segmentation.

Fig. 1

Proposed methodology

Algorithm 1

SHLAM

Fig. 2

Flowchart of SHLAM

Fig. 3

A Accuracy. B Loss

Fig. 4

Confusion matrix

Fig. 5

Comparison of accuracy

Fig. 6

Comparison of precision

Fig. 7

Comparison of recall

Fig. 8

Comparison of F-measure

Fig. 9

Comparison of error rate

Fig. 10

Comparison of Dice

Fig. 11

Comparison of Jaccard