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Alzheimer’s disease is a degenerative brain disease that impairs memory, thinking skills, and the ability to perform even the most basic tasks. The primary challenge in this domain is accurate early stage disease detection. When the disease is detected at an early stage, medical professionals can prescribe medications to reduce brain shrinkage. Although the disease may not be curable, these interventions can extend the patient’s life by slowing down the rate of shrinkage. The four cognitive states of the human brain are cognitive normal (CN), mild cognitive impairment convertible (MCIc), mild cognitive impairment non-convertible (MCInc), and Alzheimer’s disease (AD). Mild cognitive impairment convertible (MCIc) is the early stage of Alzheimer’s disease. Individuals with MCIc will develop Alzheimer’s disease for a few years. However, it is difficult to detect this state through medical investigations. The mild cognitive impairment non-convertible state (MCInc) is the state immediately before MCIc. MCInc is a common condition in people of all ages, where minor memory issues arise as a result of normal aging. Early detection of AD can be claimed if and only if the transition from MCInc to MCIc is complete. Deep learning algorithms can be promising techniques for identifying the progression stage of a disease using magnetic resonance imaging. In this study, a novel deep learning algorithm was proposed to improve the classification accuracy of MCIc vs. MCInc. This study utilized the advantages of local binary patterns along with squeeze and excitation networks (SENet). Without the squeeze and excitation network, the classification accuracy of MCIc versus MCInc was 82%. The classification accuracy improved by 86% with the use of SENet. The experimental results show that the proposed model achieves better performance for MCInc vs. MCIc classification in terms of accuracy, precision, recall, F1 score, and ROC. [ABSTRACT FROM AUTHOR]