Breast cancer is a tumor caused by the excessive growth of cells in the breast tissue or near the region of the breast. Breast cancer is most commonly found among women, and it starts developing when the cell tissues from lump of the breast become abnormal or when there is a calcium deposit in the breast. These affected cells form a large lump that consequently becomes a tumor. Digital infrared images are obtained based on the metabolism of the breast and vascular circulation of the blood flow in and around the breast region, which has more visibility than the normal breast region. In this article, we diagnose breast cancer by processing a thermal image that is acquired from thermal cameras. By analyzing the information, we can implement image processing steps to predict quantitative and qualitative information. In this work, we propose a hybrid clustering algorithm with distance measurements. The clustering step includes adaptive fuzzy k-means clustering with Chebyshev distance with improved classifiers, which include neural networks. The article discusses the experimental results along with the comparison using various metrics, such as accuracy, time, and error rates.