Classification of Gastrointestinal Cancer through Explainable AI and Ensemble Learning

Auzine, Muhammad Muzzammil; Khan, Maleika Heenaye-Mamode; Baichoo, Sunilduth; Sahib, Nuzhah Gooda; Gao, Xiaohong; Bissoonauth-Daiboo, Preeti

doi:10.1109/wids-psu57071.2023.00048

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study also thoroughly examines the characteristics that impact the classification procedure, illuminating the critical elements influencing precise lesion classification. Explainability ( 43 , 44 ) characteristics made it possible to visualize the variables that contributed to each prediction in a comprehensible way, highlighting significant differences in performance that would not have been apparent otherwise.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI-driven model for gastrointestinal cancer classification

Binzagr

2024

Front. Med.

View full text Add to dashboard Cite

Although the detection procedure has been shown to be highly effective, there are several obstacles to overcome in the usage of AI-assisted cancer cell detection in clinical settings. These issues stem mostly from the failure to identify the underlying processes. Because AI-assisted diagnosis does not offer a clear decision-making process, doctors are dubious about it. In this instance, the advent of Explainable Artificial Intelligence (XAI), which offers explanations for prediction models, solves the AI black box issue. The SHapley Additive exPlanations (SHAP) approach, which results in the interpretation of model predictions, is the main emphasis of this work. The intermediate layer in this study was a hybrid model made up of three Convolutional Neural Networks (CNNs) (InceptionV3, InceptionResNetV2, and VGG16) that combined their predictions. The KvasirV2 dataset, which comprises pathological symptoms associated to cancer, was used to train the model. Our combined model yielded an accuracy of 93.17% and an F1 score of 97%. After training the combined model, we use SHAP to analyze images from these three groups to provide an explanation of the decision that affects the model prediction.

show abstract

Section: Introductionmentioning

confidence: 99%