The lack of comprehensive explanations accompanying the results generated through automated diagnosis systems has encountered widespread mistrust among practitioners, patients, and stakeholders. This study aims to elucidate different Explainable Artificial Intelligence (XAI) strategies that can aid patients and practitioners in comprehending the results better. A set of biomedical images with different modalities served as the dataset for testing, predicting, and interpreting the predicted results. A pre-trained AI model was employed to generate predictions, which were subsequently explained using XAI techniques. Conventional criteria including accuracy, precision, recall, and F1 score were used to assess biological image analysis. Additionally, with the aid of visualization tools like feature maps and heatmaps produced by XAI techniques such as gradient-weighted class activation mapping, local interpretable model agnostic explanations, and shapley additive explanations, interpretability was observed.