A processor implemented method for detecting anatomical landmarks in abnormal subjects, the method comprising: obtaining, via one or more hardware processors, a digital radiography, DR, image of an abnormal subject, wherein the DR image comprises one or more deformed structures of the abnormal subject, and wherein the one or more deformed structures are representative of one or more abnormalities observed in the abnormal subject; filtering, using a first trained neural network executed by the one or more hardware processors, one or more artifacts from the obtained DR image to obtain a pre-processed DR image comprising the one or more deformed structures of the abnormal subject; generating, using a second trained neural network executed via the one or more hardware processors, a heat map based on the pre-processed DR image, wherein the heat map comprises one or more locations of corresponding one or more probable anatomical landmarks in the pre-processed DR image; generating, by a trained classifier executed by the one or more hardware processors, a first probability score for each of the one or more locations of the corresponding one or more probable anatomical landmarks; performing, via the one or more hardware processors, a first comparison of the first probability score of each of the one or more locations with a pre-defined threshold; selecting, via the one or more hardware processors, based on the first comparison, a subset of probable anatomical landmarks serving as a set of detected anatomical landmarks; fine-tuning a remaining subset of probable anatomical landmarks from the one or more probable anatomical landmarks, using an anatomical atlas comprised in a memory to obtain a set of fine-tuned probable anatomical landmarks, wherein the fine-tuning is performed by refining position of the remaining subset of probable anatomical landmarks; generating, by the trained classifier, a second probability score for the set of the fine-tuned probable anatomical landmarks; performing a second comparison of the second probability score of each fine-tuned probable anatomical landmark from the set of fine-tuned probable anatomical landmarks with the pre-defined threshold; and selecting, based on the second comparison, a subset of probable anatomical landmarks serving as another set of detected anatomical landmarks. obtaining, via one or more hardware processors, a digital radiography, DR, image of an abnormal subject, wherein the DR image comprises one or more deformed structures of the abnormal subject, and wherein the one or more deformed structures are representative of one or more abnormalities observed in the abnormal subject; filtering, using a first trained neural network executed by the one or more hardware processors, one or more artifacts from the obtained DR image to obtain a pre-processed DR image comprising the one or more deformed structures of the abnormal subject; generating, using a second trained neural network executed via the one or more hardware processors, a heat map based on the pre-processed DR image, wherein the heat map comprises one or more locations of corresponding one or more probable anatomical landmarks in the pre-processed DR image; generating, by a trained classifier executed by the one or more hardware processors, a first probability score for each of the one or more locations of the corresponding one or more probable anatomical landmarks; performing, via the one or more hardware processors, a first comparison of the first probability score of each of the one or more locations with a pre-defined threshold; selecting, via the one or more hardware processors, based on the first comparison, a subset of probable anatomical landmarks serving as a set of detected anatomical landmarks; fine-tuning a remaining subset of probable anatomical landmarks from the one or more probable anatomical landmarks, using an anatomical atlas comprised in a memory to obtain a set of fine-tuned probable anatomical landmarks, wherein the fine-tuning is performed by refining position of the remaining subset of probable anatomical landmarks; generating, by the trained classifier, a second probability score for the set of the fine-tuned probable anatomical landmarks; performing a second comparison of the second probability score of each fine-tuned probable anatomical landmark from the set of fine-tuned probable anatomical landmarks with the pre-defined threshold; and selecting, based on the second comparison, a subset of probable anatomical landmarks serving as another set of detected anatomical landmarks.