## 1 Introduction
Current CNN topologies employed in these systems exhibit considerable complexity, frequently integrating numerous deep layers that hinder classification tasks and elevate computational demands [2; 3]. Prior studies primarily concentrated on complex segmentation algorithms and multi-stage preprocessing methods, often adding superfluous procedural intricacy without significantly enhancing diagnostic results [4; 5]. Some studies that attempted to integrate clinical data with thermal imaging faced limitations in conclusively proving improved diagnostic accuracy [6]. Furthermore, researchers have primarily focused on creating customizedCNN architectures, neglecting the potential benefits of transfer learning and pre-trained neural networks that might boost effectiveness while reducing computing resources and training data needs [7,8]. Conventional methods of analysis often derive features from individual CNN layers, neglecting the intricate feature representations found in multi-layered deep learning structures [9]. This research introduces a novel CAD framework that utilizes transfer learning, multi-architecture CNN integration, extensive multi-layer feature extraction, and sophisticated feature transformation techniques to overcome current CAD constraints in breast cancer identification through thermal imaging. The rationale for integrating multiple lightweight CNN models arises from the fact that various CNN structures exhibit distinct inductive biases, receptive field characteristics, and hierarchical feature extraction proficiencies. Many current CAD systems depend on a singular deep learning model [27, 28, 29, 30], which may constrain the system's capacity to generalize across varied image patterns and thermal anomalies linked to breast cancer. Incorporating numerous CNNs into the framework provides architectural diversity, allowing the system to capture complementary feature representations that improve classification robustness. Each chosen CNN including MobileNet, EfficientNetB0, and ShuffleNet provides unique benefits regarding computational efficiency and feature extraction capabilities. MobileNet demonstrates superior performance in low-resource settings owing to its depthwise separable convolutions [31]; EfficientNetB0 adeptly balances depth, width, and resolution to enhance accuracy [32]; and ShuffleNet incorporates channel shuffling to improve gradient flow and feature diversity [33]. Utilizing these networks collectively enables the system to capitalize on the advantages of each while alleviating the constraints inherent in any singular architecture. This multi-model strategy enhances diagnostic performance and enhances the probability that the system will maintain efficacy across diverse imaging conditions and patient demographics. The incorporation of various CNN architectures is not simply a design decision but a strategically justified improvement intended to enhance both the accuracy and generalizability of the proposed CAD system for thermogram-based breast cancer detection. Furthermore, The study highlights that extracting features from three distinct deep layers including shallow, middle, and deep within each CNN is not simply a method for enhancing feature diversity, but a strategic feature selection process designed to identify the most discriminative representations across varying levels of abstraction. Each layer encapsulates distinct semantic information: shallow layers encode low-level textures and edges, middle layers identify intermediate patterns, and deep layers extract high-level structural features pertinent to tumor detection [34,35]. Through the analysis and comparison of the classification performance of these individual layers, the study conducts a preliminary phase of feature space optimization, determining which depth level most significantly aids in differentiating between benign and malignant cases. This multi-layer evaluation is a fundamental step towards informed feature fusion.