Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. In the recent past, the Classifiers are based on genetic signatures in which many microarray studies are analyzed to predict medical results for cancer patients. However, the Signatures from different studies have been benefitted with low-intensity ratio during the classification of individual datasets has been considered as a significant point of research in the present scenario. Hence to overcome the above discussed issue, this paper provides a Deep Learning Framework that combines an algorithm of necessary processing of Linear Discriminant Analysis (LDA) and Auto Encoder (AE) Neural Network with Long Short Term Memory is used to classify different features within the profile of gene expression for breast cancer detection. Hence, an advanced ensemble classification has been developed based on the Deep Learning (DL) algorithm to assess the clinical outcome of breast cancer. Furthermore, numerous independent breast cancer datasets and representations of the signature gene, including the primary method, have been evaluated for the optimization parameters. Finally, the experiment results show that the suggested deep learning frameworks achieve 98.27% accuracy than many other techniques such as genomic data and pathological images with multiple kernel learning (GPMKL), Multi Layer Perception (MLP), Deep Learning Diagnosis (DLD), and Spatiotemporal Wavelet Kinetics (SWK).