Focusing particularly on the lung region is crucial in various pulmonary disease classification. It helps to discard unwanted regions from the chest X-ray. This paper proposes the TIG-UNet++ model, an advanced modification of the Unet++-L4 architecture. A key feature of our model is the late introduction of a threshold image, that almost identifies the lung regions, which serves as a low-priority input to enhance feature fusion while minimizing its impact on the overall segmentation process. This approach surpassed recent studies in terms of dice, jaccard, auc, and accuracy, particularly in challenging cases with unclear or disease like tuberculosis affected images. It was tested on Montgomery, JSRT, and Shenzen datasets. Our model achieves a dice score of 98.0% on the Montgomery dataset, 97.8% on the JSRT dataset, 96.1% on the combination of Montgomery and Shenzen, and 97.3% on the combination of all the dataset, demonstrating its robustness and effectiveness. These outstanding scores were obtained due to the late introduction of the thresholded image of the input that was passed additionally to the model along with the original chest x-ray image.