## 6 Conclusions  
The manual detection and classification of GI diseases is a challenging task; therefore, an automated system is needed for improved results. In this work, we proposed a DL-based architecture to accurately segment and classify GI diseases. The main idea is to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline Logistic and ridge regression [17] & 83.3\% \\ \hline CNN-based framework [18] & 85.69\% \\ \hline Various classifiers are applied to multiple handcrafted extracted features [13] & 93.64\% \\ \hline Modified VGGNet model on preprocessed images [20] & 86.6\% \\ \hline Divided images into numerous regions and then applied the modified DenseNet Model [22] & 94.03\% \\ \hline Maximizing the characteristics gleaned from two pre-trained models [28] & 96.43\% \\ \hline A contrast enhancement approach is suggested with MobileNet-V2 [29] & 96.40\% \\ \hline An attention image-based classification is performed and best features selected [3] & 98.07\% \\ \hline
**Proposed Model** & **98.32\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Proposed model comparison with other approaches.  
perform localization using an encoder-decoder-based segmentation technique and draw contours around the diseased area of an image. Furthermore, heat maps are generated using Grad-CAM for unsegmented and segmented images to visualize the high-magnitude region within an image. The images with contours are then used for classification using a deep learning-based model. Segmentation performance is evaluated using various performance metrics like dice, mIOU, accuracy, precision, and recall. For segmentation, our proposed model achieved 82.08% dice, 90.30% mIOU, 94.35% precision, and 85.97% recall. For classification, we reported effectiveness in terms of accuracy, precision, and recall rate. The proposed model achieved 98.32% accuracy, 96.13% recall, and 99.68% precision using the softmax classifier. Our findings show that the presented model did not treat any disease case as normal, which is crucial when human life is involved. Although the proposed model achieved better results as compared to the existing state-of-the-art techniques, several interesting questions need to be researched in the future. For instance, the effects of contrast enhancement and illumination variation were not considered in this research. These preprocessing steps will be the focus of future work, as these highlight the region of interest, and may result in improved performance. Furthermore, we plan to assess the performance of the proposed method in diverse domains, such as those mentioned in references [49; 50; 51; 52; 53].  
All authors contributed equally to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the International Cooperative RD program (Project No. P0016038); the MSIT (Ministry of Science and ICT), Republic of Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-RS-2022-00156354) supervised by the IITP (Institute for Information Communications Technology Planning and Evaluation); Institute of Information & communications Technology Planning & Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2023-RS-2023-00254529) grant funded by the Korea government(MSIT); and the faculty research fund of Sejong University in 2022.  
Not applicable.  
Research is conducted on publically available dataset.  
The authors declare no conflict of interest.