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Chip-scale integrated imaging spectrometers show significant potential for high-performance spectral analysis due to advancements in fabrication and computational techniques. Many practical applications, such as astronomy and molecular spectroscopy, require analyzing light at sub-nanowatt levels, where inherent enhancement in spectrometer signals can reduce the need for expensive photodetectors or long integration time. Previously, we introduced an integrated spectrometer scheme using machine learning to reconstruct spectra from imaging the wavelength-dependent patterns scattered out of a multimode interference (MMI) waveguide. In this work, we report a signal enhancement of 13.6 dB and an increase of device sensitivity and dynamic range by 15 dB by selective roughening of the waveguide surface via plasma etching. By imaging interference patterns at various points along the waveguide, we determine that the best spectrometer performance is achieved by imaging MMI sections with highest pattern variation. We report accurate spectral measurements using convolutional neural network-based spectral reconstruction with 1 nm resolution at input powers as low as 300 pW for the present experimental configuration, and a scattering coefficient of 1.109 cm^-1from the etched section.