###### Abstract
However, due to excessive cost and limited availability, F-FDG PET/CT application is severely constrained. PET itself is also a rather expensive technology, and neither PET scanners nor the cyclotrons required to create isotopes for PETs are generally accessible. For the overall evaluation of breast lesions, SPECT imaging has a higher diagnostic value than mammography, has been extensively validated, and has a high sensitivity. However, SPECT is much more expensive and less widely available than other techniques [3; 4; 5; 6; 7; 8]. Finally, there is a method called contrast-enhanced spectral mammography (CESM), which can be used to detect breast cancer and provides low-energy mammographic images comparable to standard digital mammography, as well as a post-contrast recombined image to evaluate tumor neovascularization, like magnetic resonance imaging (MRI). This technique, however, has limitations, such as the usage of an iodinated contrast agent, and increased radiation exposure [7]. Thus, every technique has its own advantages and disadvantages. The main disadvantages of the considered methods are that they are not practical for regular mass screening frequently, and some of them have limited access for people who live in remote areas of a country. Thermography is one of the non-invasive and affordable techniques for routine and bulk screening [5; 6]. It is common knowledge that many health problems can be accurately detected by a person's body temperature. Blood perfusion, metabolic rate, and ambient temperature are just a few examples of the variables that affect how hot or cold the body is. Thermography could detect any temperature abnormality in the body, such as a tumor, as most tumors cause temperature changes in the surrounding tissue [5; 6; 7; 8]. Thermography is a type of imaging that uses infrared (IR) light to create colored images of temperature distributions. According to the studies [8] conducted in this field, the surface of a breast with cancerous tissues has a higher temperature profile than the surrounding region, and abnormalities can be discovered through thermography. The diagnosis of thermography is reliant on qualitative principles and human judgment, such as the asymmetry of two breasts, hyper-hermetic patterns, and atypical vascular patterns, even though thermography has straightforward functioning principles [9]. By examining the temperature distribution and randomly matching the temperature profiles at several locations, quantitative information is often manually derived. With the quick advancement of computer technology, computer-aided tools can be used to help with the diagnosis by supporting the interpretation of thermal images, creating better breast models, and automatically identifying the locations and sizes of tumors, as well as other unique characteristics of breast tissues [10]. The convolutional neural network (CNN), a deep-learning neural network of neurons with learnable weights and biases, is one newly discovered technique for recognizing images. The practice of identifying images based on their visual content is known as image classification. Recognizing breast thermograms with a predefined label is a crucial step in the learning process for neural networks. This is referred to as supervised learning. Without any human expert intervention, a diagnostic tool can be built using CNN and CNN-based models to classify \"healthy\" and \"sick\" types of thermograms as diagnosed by doctors. This study examines various CNN-based models with varying parameters and develops an efficient CNN model for binary classification using breast thermograms. In the recent developments in the usage of state-of-the-art and transfer learning for medical images, several studies have shown results in breast cancer thermography. For instance, in the study by [PERSON] [11] they achieved an accuracy of 92% by using CNN with fifty-seven patients available data. Additionally, research by [PERSON] [12] demonstrated an accuracy of 91.2% by exploiting the VGG-16 model, with 173 patients in total.