## 2 Previous Works
An evolutionary algorithm was employed to allocate performance-based weights, enhancing the overall diagnosis. Furthermore, adaptive segmentation customized for individual patient variations in temperature was executed, employing hybrid approaches that capitalize on particular heat segments. A comprehensive method that inputs complete thermograms into CNNs produced notable outcomes, with one model attaining an accuracy score of 94 percent [43]. Conversely, lightweight models such as SqueezeNet, specifically optimized for breast thermogram analysis, were integrated with hybrid optimization algorithms, incorporating Genetic Algorithms and Grey Wolf Optimizers. This method exhibited exceptional effectiveness, attaining an accuracy of 100 percent yet employing merely 3% of the attributes gathered [44]. In recent years, recurrent neural networks (RNNs) have received immense hype owing to their capability of modeling sequential data and capturing temporal dependencies. In the field of medical diagnosis, RNNs have a reputation for time-series analysis of physiological signals or longitudinal studies in imaging. RNNs and their advanced variants, including long short-term memory networks (LSTM) and gated recurrent units (GRU), have shown promising capabilities to capture temporal dependencies and sequential patterns in medical imaging. Recent work has claimed efficacy for RNNs in dynamic feature extraction, which could complement CNN-based approaches by combining spatial and temporal correlation modeling in thermographic sequences. The inclusion of RNNs would encourage a wider range of new deductions about the changes in the temporal patterns in the frames, thus being able to more sensitively detect subtle changes associated with malignancies. For example, in the study [45], hybrid convolution-recurrent neural network (CNN-RNN) models were evaluated in terms of five state-of-the-art pre-trained CNN architectures merged with three RNNs for detecting tumor abnormalities in dynamic breast thermographic images. The hybrid architecture that favored breast cancer detection is VGG16-LSTM with accuracy of 95.72%, and sensitivity and specificity of 92.76% and 98.68%, respectively. Thus hybrid CNN-RNN models showed better performance than standalone CNN models, which means temporal data recovery from dynamic breast thermographs is possible. Similarly, the article [46] provided a new hybrid model combining CNN and LSTM using two datasets accessible from the Kaggle repository for binary breast cancer classification. CNN obtained mammographic features which include spatial hierarchies and malignancy patterns; LSTM networks defined sequential dependencies and temporal interactions instead. Combining these components improved resilience and classification accuracy. Other deep learning models including standalone CNN, LSTM, GRUs, VGG-16, and ResNet-50 were evaluated against the proposed model. CNN-LSTM model showed better performance with accuracy of 99.17% and 99.90% on respective datasets. The literature on CAD tools for breast cancer diagnosis identifies significant deficiencies that restrict the creation of effective and precise tools for diagnosis. A notable limitation in current research is the dependence on complicated CNN designs with multiple deep layers, which substantially elevate computational requirements and complicate classification processes [29; 41; 43; 46]. These intricate models frequently result in overfitting and necessitate substantial training data, rendering them less feasible for use in practice. Furthermore, numerous studies concentrate on individual CNN structures [27; 28; 29; 30], overlooking the advantages of merging various models to boost feature diversity while improving diagnostic precision. Moreover, traditional approaches generally extract features from singular CNN layers [41; 45; 46], inadequately leveraging the complex feature representations present in multi-layered deep learning architectures.