This paper devises a technique for diagnosing and classifying osteoporosis using the femur bone's X-ray images. The devised approach uses the following phases: image acquisition, pre-processing, segmentation of femur boundary, measurement of femoral geometry, feature mining, and osteoporosis categorization. Here, femur boundary segmentation is done using the Deep Contour Aware Network (DCAN), which is trained by the Chronological Artificial Ecosystem Optimization (CAEO) algorithm. Then, the femoral geometry is measured and the most relevant features in the image are identified. Further, osteoporosis is classified using the Convolutional Neural Network (CNN) with Transfer Learning (TL), where the CNN is employed with the hyperparameters of the pre-trained model, LeNet. Here, the Chronological Artificial Ecosystem Tasmanian Devil Optimization (CAETDO) algorithm optimizes the classification process. The CAETDO-CNN-TL is examined based on metrics and have the values of 0.910, 0.927, 0.928, and 0.904 for precision, True Negative Rate (TNR), True Positive Rate (TPR), and accuracy, correspondingly. [ABSTRACT FROM AUTHOR]